Patent Publication Number: US-11049250-B2

Title: Systems and methods to deliver point of care alerts for radiological findings

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent arises from U.S. patent application Ser. No. 15/821,161, which was filed on Nov. 22, 2017. U.S. patent application Ser. No. 15/821,161 is hereby incorporated herein by reference in its entirety. Priority to U.S. patent application Ser. No. 15/821,161 is hereby claimed. 
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to improved medical systems and, more particularly, to improved learning systems and methods for medical image processing. 
     BACKGROUND 
     A variety of economy, operational, technological, and administrative hurdles challenge healthcare facilities, such as hospitals, clinics, doctors&#39; offices, imaging centers, teleradiology, etc., to provide quality care to patients. Economic drivers, less skilled staff, fewer staff, complicated equipment, and emerging accreditation for controlling and standardizing radiation exposure dose usage across a healthcare enterprise create difficulties for effective management and use of imaging and information systems for examination, diagnosis, and treatment of patients. 
     Healthcare provider consolidations create geographically distributed hospital networks in which physical contact with systems is too costly. At the same time, referring physicians want more direct access to supporting data in reports along with better channels for collaboration. Physicians have more patients, less time, and are inundated with huge amounts of data, and they are eager for assistance. 
     Healthcare provider (e.g., x-ray technologist, doctor, nurse, etc.) tasks including image processing and analysis, quality assurance/quality control, etc., are time consuming and resource intensive tasks impractical, if not impossible, for humans to accomplish alone. 
     BRIEF SUMMARY 
     Certain examples provide apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings in image data, and generation of notification at or near a point of care for a patient. 
     Certain examples provide an imaging apparatus including a memory including chest image data and instructions and a processor. The example processor is to execute the instructions to at least: process the chest image data using a trained learning network in real time after acquisition of the chest image data to identify a pneumothorax in the chest image data; receive feedback regarding the identification of the pneumothorax; and, when the feedback confirms the identification of the pneumothorax, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the pneumothorax and prompt a responsive action with respect to a patient associated with the chest image data. 
     Certain examples provide a computer-readable storage medium in an imaging apparatus including instructions. The instructions, when executed, cause at least one processor in the imaging apparatus to at least: process the chest image data using a trained learning network in real time after acquisition of the chest image data to identify a pneumothorax in the chest image data; receive feedback regarding the identification of the pneumothorax; and, when the feedback confirms the identification of the pneumothorax, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the pneumothorax and prompt a responsive action with respect to a patient associated with the chest image data. 
     Certain examples provide a computer-implemented method including processing, by executing an instruction with at least one processor, the chest image data using a trained learning network in real time after acquisition of the chest image data to identify a pneumothorax in the chest image data. The example method includes processing, by executing an instruction with the at least one processor, feedback regarding the identification of the pneumothorax. The example method includes, when the feedback confirms the identification of the pneumothorax, triggering, by executing an instruction with the at least one processor, a notification at an imaging apparatus to notify a healthcare practitioner regarding the pneumothorax and prompt a responsive action with respect to a patient associated with the chest image data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-1B  illustrate an example imaging system to which the methods, apparatus, and articles of manufacture disclosed herein can be applied. 
         FIG. 2  illustrates an example mobile imaging system. 
         FIG. 3  is a representation of an example learning neural network. 
         FIG. 4  illustrates a particular implementation of the example neural network as a convolutional neural network. 
         FIG. 5  is a representation of an example implementation of an image analysis convolutional neural network. 
         FIG. 6A  illustrates an example configuration to apply a learning network to process and/or otherwise evaluate an image. 
         FIG. 6B  illustrates a combination of a plurality of learning networks. 
         FIG. 7  illustrates example training and deployment phases of a learning network. 
         FIG. 8  illustrates an example product leveraging a trained network package to provide a deep learning product offering. 
         FIGS. 9A-9C  illustrate various deep learning device configurations. 
         FIG. 10  illustrates an example image processing system or apparatus. 
         FIGS. 11-12  illustrate flow diagrams for example methods of automated processing and image analysis to present findings at the point of care in accordance with the systems and/or apparatus of  FIGS. 1-10 . 
         FIGS. 13-24  illustrate example displays to provide output and facilitate interaction in accordance with the apparatus, systems, and methods described above in connection with  FIGS. 1-12 . 
         FIG. 25  illustrates an example system configuration in which an imaging system interfaces with a broker device to communicate with a plurality of information systems. 
         FIG. 26  illustrates an example system configuration in which an artificial intelligent model executes on an edge device to provide point of care alerts on an imaging machine. 
         FIG. 27  illustrates an example system to incorporate and compare artificial intelligence model processing results between current and prior exams. 
         FIG. 28  illustrates a flow diagrams for an example method to prioritize, in a worklist, an exam related to a critical finding for review. 
         FIG. 29  illustrates a flow diagram for an example method to compare current and prior AI analyses of image data to generate a notification for a point of care alert. 
         FIG. 30  is a block diagram of a processor platform structured to execute the example machine readable instructions to implement components disclosed and described herein. 
     
    
    
     The foregoing summary, as well as the following detailed description of certain embodiments of the present invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, certain embodiments are shown in the drawings. It should be understood, however, that the present invention is not limited to the arrangements and instrumentality shown in the attached drawings. The figures are not scale. Wherever possible, the same reference numbers will be used throughout the drawings and accompanying written description to refer to the same or like parts. 
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific examples that may be practiced. These examples are described in sufficient detail to enable one skilled in the art to practice the subject matter, and it is to be understood that other examples may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the subject matter of this disclosure. The following detailed description is, therefore, provided to describe an exemplary implementation and not to be taken as limiting on the scope of the subject matter described in this disclosure. Certain features from different aspects of the following description may be combined to form yet new aspects of the subject matter discussed below. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 
     While certain examples are described below in the context of medical or healthcare systems, other examples can be implemented outside the medical environment. For example, certain examples can be applied to non-medical imaging such as non-destructive testing, explosive detection, etc. 
     I. Overview 
     Imaging devices (e.g., gamma camera, positron emission tomography (PET) scanner, computed tomography (CT) scanner, X-Ray machine, fluoroscopy machine, magnetic resonance (MR) imaging machine, ultrasound scanner, etc.) generate medical images (e.g., native Digital Imaging and Communications in Medicine (DICOM) images) representative of the parts of the body (e.g., organs, tissues, etc.) to diagnose and/or treat diseases. Medical images may include volumetric data including voxels associated with the part of the body captured in the medical image. Medical image visualization software allows a clinician to segment, annotate, measure, and/or report functional or anatomical characteristics on various locations of a medical image. In some examples, a clinician may utilize the medical image visualization software to identify regions of interest with the medical image. 
     Acquisition, processing, quality control, analysis, and storage of medical image data play an important role in diagnosis and treatment of patients in a healthcare environment. A medical imaging workflow and devices involved in the workflow can be configured, monitored, and updated throughout operation of the medical imaging workflow and devices. Machine and/or deep learning can be used to help configure, monitor, and update the medical imaging workflow and devices. 
     Certain examples provide and/or facilitate improved imaging devices which improve diagnostic accuracy and/or coverage. Certain examples facilitate improved image reconstruction and further processing to provide improved diagnostic accuracy. 
     Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to locate an object in an image, understand speech and convert speech into text, and improve the relevance of search engine results, for example. Deep learning is a subset of machine learning that uses a set of algorithms to model high-level abstractions in data using a deep graph with multiple processing layers including linear and non-linear transformations. While many machine learning systems are seeded with initial features and/or network weights to be modified through learning and updating of the machine learning network, a deep learning network trains itself to identify “good” features for analysis. Using a multilayered architecture, machines employing deep learning techniques can process raw data better than machines using conventional machine learning techniques. Examining data for groups of highly correlated values or distinctive themes is facilitated using different layers of evaluation or abstraction. 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “deep learning” is a machine learning technique that utilizes multiple data processing layers to recognize various structures in data sets and classify the data sets with high accuracy. A deep learning network can be a training network (e.g., a training network model or device) that learns patterns based on a plurality of inputs and outputs. A deep learning network can be a deployed network (e.g., a deployed network model or device) that is generated from the training network and provides an output in response to an input. 
     The term “supervised learning” is a deep learning training method in which the machine is provided already classified data from human sources. The term “unsupervised learning” is a deep learning training method in which the machine is not given already classified data but makes the machine useful for abnormality detection. The term “semi-supervised learning” is a deep learning training method in which the machine is provided a small amount of classified data from human sources compared to a larger amount of unclassified data available to the machine. 
     The term “representation learning” is a field of methods for transforming raw data into a representation or feature that can be exploited in machine learning tasks. In supervised learning, features are learned via labeled input. 
     The term “convolutional neural networks” or “CNNs” are biologically inspired networks of interconnected data used in deep learning for detection, segmentation, and recognition of pertinent objects and regions in datasets. CNNs evaluate raw data in the form of multiple arrays, breaking the data in a series of stages, examining the data for learned features. 
     The term “transfer learning” is a process of a machine storing the information used in properly or improperly solving one problem to solve another problem of the same or similar nature as the first. Transfer learning may also be known as “inductive learning”. Transfer learning can make use of data from previous tasks, for example. 
     The term “active learning” is a process of machine learning in which the machine selects a set of examples for which to receive training data, rather than passively receiving examples chosen by an external entity. For example, as a machine learns, the machine can be allowed to select examples that the machine determines will be most helpful for learning, rather than relying only an external human expert or external system to identify and provide examples. 
     The term “computer aided detection” or “computer aided diagnosis” refer to computers that analyze medical images for the purpose of suggesting a possible diagnosis. 
     Certain examples use neural networks and/or other machine learning to implement a new workflow for image and associated patient analysis including generating alerts based on radiological findings may be generated and delivered at the point of care of a radiology exam. Certain examples use Artificial Intelligence (AI) algorithms to immediately (e.g., with a data processing, transmission, and/or storage/retrieval latency) process a radiological exam (e.g., an image or set of images), and provide an alert based on the automated exam analysis at the point of care. The alert and/or other notification can be seen on a visual display, represented by a sensor (e.g., light color, etc.), be an audible noise/tone, and/or be sent as a message (e.g., short messaging service (SMS), Health Level 7 (HL7), DICOM header tag, phone call, etc.). The alerts may be intended for the technologist acquiring the exam, clinical team providers (e.g., nurse, doctor, etc.), radiologist, administration, operations, and/or even the patient. The alerts may be to indicate a specific or multiple quality control and/or radiological finding(s) or lack thereof in the exam image data, for example. 
     In certain examples, the AI algorithm can be (1) embedded within the radiology system, (2) running on a mobile device (e.g., a tablet, smart phone, laptop, other handheld or mobile computing device, etc.), and/or (3) running in a cloud (e.g., on premise or off premise) and delivers the alert via a web browser (e.g., which may appear on the radiology system, mobile device, computer, etc.). Such configurations can be vendor neutral and compatible with legacy imaging systems. For example, if the AI processor is running on a mobile device and/or in the “cloud”, the configuration can receive the images (A) from the x-ray and/or other imaging system directly (e.g., set up as secondary push destination such as a Digital Imaging and Communications in Medicine (DICOM) node, etc.), (B) by tapping into a Picture Archiving and Communication System (PACS) destination for redundant image access, (C) by retrieving image data via a sniffer methodology (e.g., to pull a DICOM image off the system once it is generated), etc. 
     Deep Learning and Other Machine Learning 
     Deep learning is a class of machine learning techniques employing representation learning methods that allows a machine to be given raw data and determine the representations needed for data classification. Deep learning ascertains structure in data sets using backpropagation algorithms which are used to alter internal parameters (e.g., node weights) of the deep learning machine. Deep learning machines can utilize a variety of multilayer architectures and algorithms. While machine learning, for example, involves an identification of features to be used in training the network, deep learning processes raw data to identify features of interest without the external identification. 
     Deep learning in a neural network environment includes numerous interconnected nodes referred to as neurons. Input neurons, activated from an outside source, activate other neurons based on connections to those other neurons which are governed by the machine parameters. A neural network behaves in a certain manner based on its own parameters. Learning refines the machine parameters, and, by extension, the connections between neurons in the network, such that the neural network behaves in a desired manner. 
     Deep learning that utilizes a convolutional neural network segments data using convolutional filters to locate and identify learned, observable features in the data. Each filter or layer of the CNN architecture transforms the input data to increase the selectivity and invariance of the data. This abstraction of the data allows the machine to focus on the features in the data it is attempting to classify and ignore irrelevant background information. 
     Deep learning operates on the understanding that many datasets include high level features which include low level features. While examining an image, for example, rather than looking for an object, it is more efficient to look for edges which form motifs which form parts, which form the object being sought. These hierarchies of features can be found in many different forms of data such as speech and text, etc. 
     Learned observable features include objects and quantifiable regularities learned by the machine during supervised learning. A machine provided with a large set of well classified data is better equipped to distinguish and extract the features pertinent to successful classification of new data. 
     A deep learning machine that utilizes transfer learning may properly connect data features to certain classifications affirmed by a human expert. Conversely, the same machine can, when informed of an incorrect classification by a human expert, update the parameters for classification. Settings and/or other configuration information, for example, can be guided by learned use of settings and/or other configuration information, and, as a system is used more (e.g., repeatedly and/or by multiple users), a number of variations and/or other possibilities for settings and/or other configuration information can be reduced for a given situation. 
     An example deep learning neural network can be trained on a set of expert classified data, classified and further annotated for object localization, for example. This set of data builds the first parameters for the neural network, and this would be the stage of supervised learning. During the stage of supervised learning, the neural network can be tested whether the desired behavior has been achieved. 
     Once a desired neural network behavior has been achieved (e.g., a machine has been trained to operate according to a specified threshold, etc.), the machine can be deployed for use (e.g., testing the machine with “real” data, etc.). During operation, neural network classifications can be confirmed or denied (e.g., by an expert user, expert system, reference database, etc.) to continue to improve neural network behavior. The example neural network is then in a state of transfer learning, as parameters for classification that determine neural network behavior are updated based on ongoing interactions. In certain examples, the neural network can provide direct feedback to another process. In certain examples, the neural network outputs data that is buffered (e.g., via the cloud, etc.) and validated before it is provided to another process. 
     Deep learning machines using convolutional neural networks (CNNs) can be used for image analysis. Stages of CNN analysis can be used for facial recognition in natural images, computer-aided diagnosis (CAD), etc. 
     High quality medical image data can be acquired using one or more imaging modalities, such as x-ray, computed tomography (CT), molecular imaging and computed tomography (MICT), magnetic resonance imaging (MRI), etc. Medical image quality is often not affected by the machines producing the image but the patient. A patient moving during an MRI can create a blurry or distorted image that can prevent accurate diagnosis, for example. 
     Interpretation of medical images, regardless of quality, is only a recent development. Medical images are largely interpreted by physicians, but these interpretations can be subjective, affected by the condition of the physician&#39;s experience in the field and/or fatigue. Image analysis via machine learning can support a healthcare practitioner&#39;s workflow. 
     Deep learning machines can provide computer aided detection support to improve their image analysis with respect to image quality and classification, for example. However, issues facing deep learning machines applied to the medical field often lead to numerous false classifications. Deep learning machines must overcome small training datasets and require repetitive adjustments, for example. 
     Deep learning machines, with minimal training, can be used to determine the quality of a medical image, for example. Semi-supervised and unsupervised deep learning machines can be used to quantitatively measure qualitative aspects of images. For example, deep learning machines can be utilized after an image has been acquired to determine if the quality of the image is sufficient for diagnosis. Supervised deep learning machines can also be used for computer aided diagnosis. Supervised learning can help reduce susceptibility to false classification, for example. 
     Deep learning machines can utilize transfer learning when interacting with physicians to counteract the small dataset available in the supervised training. These deep learning machines can improve their computer aided diagnosis over time through training and transfer learning. 
     II. Description of Examples 
     Example Imaging Systems 
     The methods, apparatus, and articles of manufacture described herein can be applied to a variety of healthcare and non-healthcare systems. In one particular example, the methods, apparatus, and articles of manufacture described herein can be applied to the components, configuration, and operation of a computed tomography (CT) imaging system.  FIGS. 1A-1B  illustrate an example implementation of a CT imaging scanner to which the methods, apparatus, and articles of manufacture disclosed herein can be applied.  FIGS. 1A and 1B  show a CT imaging system  10  including a gantry  12 . Gantry  12  has a rotary member  13  with an x-ray source  14  that projects a beam of x-rays  16  toward a detector assembly  18  on the opposite side of the rotary member  13 . A main bearing may be utilized to attach the rotary member  13  to the stationary structure of the gantry  12 . X-ray source  14  includes either a stationary target or a rotating target. Detector assembly  18  is formed by a plurality of detectors  20  and data acquisition systems (DAS)  22 , and can include a collimator. The plurality of detectors  20  sense the projected x-rays that pass through a subject  24 , and DAS  22  converts the data to digital signals for subsequent processing. Each detector  20  produces an analog or digital electrical signal that represents the intensity of an impinging x-ray beam and hence the attenuated beam as it passes through subject  24 . During a scan to acquire x-ray projection data, rotary member  13  and the components mounted thereon can rotate about a center of rotation. 
     Rotation of rotary member  13  and the operation of x-ray source  14  are governed by a control mechanism  26  of CT system  10 . Control mechanism  26  can include an x-ray controller  28  and generator  30  that provides power and timing signals to x-ray source  14  and a gantry motor controller  32  that controls the rotational speed and position of rotary member  13 . An image reconstructor  34  receives sampled and digitized x-ray data from DAS  22  and performs high speed image reconstruction. The reconstructed image is output to a computer  36  which stores the image in a computer storage device  38 . 
     Computer  36  also receives commands and scanning parameters from an operator via operator console  40  that has some form of operator interface, such as a keyboard, mouse, touch sensitive controller, voice activated controller, or any other suitable input apparatus. Display  42  allows the operator to observe the reconstructed image and other data from computer  36 . The operator supplied commands and parameters are used by computer  36  to provide control signals and information to DAS  22 , x-ray controller  28 , and gantry motor controller  32 . In addition, computer  36  operates a table motor controller  44  which controls a motorized table  46  to position subject  24  and gantry  12 . Particularly, table  46  moves a subject  24  through a gantry opening  48 , or bore, in whole or in part. A coordinate system  50  defines a patient or Z-axis  52  along which subject  24  is moved in and out of opening  48 , a gantry circumferential or X-axis  54  along which detector assembly  18  passes, and a Y-axis  56  that passes along a direction from a focal spot of x-ray tube  14  to detector assembly  18 . 
     Thus, certain examples can apply machine learning techniques to configuration and/or operation of the CT scanner  10  and its gantry  12 , rotary member  13 , x-ray source  14 , detector assembly  18 , control mechanism  26 , image reconstructor  34 , computer  36 , operator console  40 , display  42 , table controller  44 , table  46 , and/or gantry opening  48 , etc. Component configuration, operation, etc., can be monitored based on input, desired output, actual output, etc., to learn and suggest change(s) to configuration, operation, and/or image capture and/or processing of the scanner  10  and/or its components, for example. 
       FIG. 2  illustrates a portable variant of an x-ray imaging system  200 . The example digital mobile x-ray system  200  can be positioned with respect to a patient bed without requiring the patient to move and reposition themselves on the patient table  46  of a stationary imaging system  10 . Wireless technology enables wireless communication (e.g., with adaptive, automatic channel switching, etc.) for image and/or other data transfer to and from the mobile imaging system  200 . Digital images can be obtained and analyzed at the imaging system  200  and/or transferred to another system (e.g., a PACS, etc.) for further analysis, annotation, storage, etc. 
     The mobile imaging system  200  includes a source  202  and a wireless detector  204  that can be positioned underneath and/or otherwise with respect to a patient anatomy to be imaged. The example mobile system  200  also includes a display  206  to display results of image acquisition from the wireless detector  204 . The example mobile system  200  includes a processor  210  to configure and control image acquisition, image processing, image data transmission, etc. 
     In some examples, the imaging system  10 ,  200  can include a computer and/or other processor  36 ,  210  to process obtained image data at the imaging system  10 ,  200 . For example, the computer and/or other processor  36 ,  210  can implement an artificial neural network and/or other machine learning construct to process acquired image data and output an analysis, alert, and/or other result. 
     Example Learning Network Systems 
       FIG. 3  is a representation of an example learning neural network  300 . The example neural network  300  includes layers  320 ,  340 ,  360 , and  380 . The layers  320  and  340  are connected with neural connections  330 . The layers  340  and  360  are connected with neural connections  350 . The layers  360  and  380  are connected with neural connections  370 . Data flows forward via inputs  312 ,  314 ,  316  from the input layer  320  to the output layer  380  and to an output  390 . 
     The layer  320  is an input layer that, in the example of  FIG. 3 , includes a plurality of nodes  322 ,  324 ,  326 . The layers  340  and  360  are hidden layers and include, the example of  FIG. 3 , nodes  342 ,  344 ,  346 ,  348 ,  362 ,  364 ,  366 ,  368 . The neural network  300  may include more or less hidden layers  340  and  360  than shown. The layer  380  is an output layer and includes, in the example of  FIG. 3 , a node  382  with an output  390 . Each input  312 - 316  corresponds to a node  322 - 326  of the input layer  320 , and each node  322 - 326  of the input layer  320  has a connection  330  to each node  342 - 348  of the hidden layer  340 . Each node  342 - 348  of the hidden layer  340  has a connection  350  to each node  362 - 368  of the hidden layer  360 . Each node  362 - 368  of the hidden layer  360  has a connection  370  to the output layer  380 . The output layer  380  has an output  390  to provide an output from the example neural network  300 . 
     Of connections  330 ,  350 , and  370  certain example connections  332 ,  352 ,  372  may be given added weight while other example connections  334 ,  354 ,  374  may be given less weight in the neural network  300 . Input nodes  322 - 326  are activated through receipt of input data via inputs  312 - 316 , for example. Nodes  342 - 348  and  362 - 368  of hidden layers  340  and  360  are activated through the forward flow of data through the network  300  via the connections  330  and  350 , respectively. Node  382  of the output layer  380  is activated after data processed in hidden layers  340  and  360  is sent via connections  370 . When the output node  382  of the output layer  380  is activated, the node  382  outputs an appropriate value based on processing accomplished in hidden layers  340  and  360  of the neural network  300 . 
       FIG. 4  illustrates a particular implementation of the example neural network  300  as a convolutional neural network  400 . As shown in the example of  FIG. 4 , an input  310  is provided to the first layer  320  which processes and propagates the input  310  to the second layer  340 . The input  310  is further processed in the second layer  340  and propagated to the third layer  360 . The third layer  360  categorizes data to be provided to the output layer e 80 . More specifically, as shown in the example of  FIG. 4 , a convolution  404  (e.g., a 5×5 convolution, etc.) is applied to a portion or window (also referred to as a “receptive field”)  402  of the input  310  (e.g., a 32×32 data input, etc.) in the first layer  320  to provide a feature map  406  (e.g., a (6×) 28×28 feature map, etc.). The convolution  404  maps the elements from the input  310  to the feature map  406 . The first layer  320  also provides subsampling (e.g., 2×2 subsampling, etc.) to generate a reduced feature map  410  (e.g., a (6×) 14×14 feature map, etc.). The feature map  410  undergoes a convolution  412  and is propagated from the first layer  320  to the second layer  340 , where the feature map  410  becomes an expanded feature map  414  (e.g., a (16×) 10×10 feature map, etc.). After subsampling  416  in the second layer  340 , the feature map  414  becomes a reduced feature map  418  (e.g., a (16×) 4×5 feature map, etc.). The feature map  418  undergoes a convolution  420  and is propagated to the third layer  360 , where the feature map  418  becomes a classification layer  422  forming an output layer of N categories  424  with connection  426  to the convoluted layer  422 , for example. 
       FIG. 5  is a representation of an example implementation of an image analysis convolutional neural network  500 . The convolutional neural network  500  receives an input image  502  and abstracts the image in a convolution layer  504  to identify learned features  510 - 522 . In a second convolution layer  530 , the image is transformed into a plurality of images  530 - 538  in which the learned features  510 - 522  are each accentuated in a respective sub-image  530 - 538 . The images  530 - 538  are further processed to focus on the features of interest  510 - 522  in images  540 - 548 . The resulting images  540 - 548  are then processed through a pooling layer which reduces the size of the images  540 - 548  to isolate portions  550 - 554  of the images  540 - 548  including the features of interest  510 - 522 . Outputs  550 - 554  of the convolutional neural network  500  receive values from the last non-output layer and classify the image based on the data received from the last non-output layer. In certain examples, the convolutional neural network  500  may contain many different variations of convolution layers, pooling layers, learned features, and outputs, etc. 
       FIG. 6A  illustrates an example configuration  600  to apply a learning (e.g., machine learning, deep learning, etc.) network to process and/or otherwise evaluate an image. Machine learning can be applied to a variety of processes including image acquisition, image reconstruction, image analysis/diagnosis, etc. As shown in the example configuration  600  of  FIG. 6A , raw data  610  (e.g., raw data  610  such as sonogram raw data, etc., obtained from an imaging scanner such as an x-ray, computed tomography, ultrasound, magnetic resonance, etc., scanner) is fed into a learning network  620 . The learning network  620  processes the data  610  to correlate and/or otherwise combine the raw data  620  into processed data  630  (e.g., a resulting image, etc.) (e.g., a “good quality” image and/or other image providing sufficient quality for diagnosis, etc.). The learning network  620  includes nodes and connections (e.g., pathways) to associate raw data  610  with the processed data  630 . The learning network  620  can be a training network that learns the connections and processes feedback to establish connections and identify patterns, for example. The learning network  620  can be a deployed network that is generated from a training network and leverages the connections and patterns established in the training network to take the input raw data  610  and generate the resulting image  630 , for example. 
     Once the learning  620  is trained and produces good images  630  from the raw image data  610 , the network  620  can continue the “self-learning” process and refine its performance as it operates. For example, there is “redundancy” in the input data (raw data)  610  and redundancy in the network  620 , and the redundancy can be exploited. 
     If weights assigned to nodes in the learning network  620  are examined, there are likely many connections and nodes with very low weights. The low weights indicate that these connections and nodes contribute little to the overall performance of the learning network  620 . Thus, these connections and nodes are redundant. Such redundancy can be evaluated to reduce redundancy in the inputs (raw data)  610 . Reducing input  610  redundancy can result in savings in scanner hardware, reduced demands on components, and also reduced exposure dose to the patient, for example. 
     In deployment, the configuration  600  forms a package  600  including an input definition  610 , a trained network  620 , and an output definition  630 . The package  600  can be deployed and installed with respect to another system, such as an imaging system, analysis engine, etc. An image enhancer  625  can leverage and/or otherwise work with the learning network  620  to process the raw data  610  and provide a result (e.g., processed image data and/or other processed data  630 , etc.). The pathways and connections between nodes of the trained learning network  620  enable the image enhancer  625  to process the raw data  610  to form the image and/or other processed data result  630 , for example. 
     As shown in the example of  FIG. 6B , the learning network  620  can be chained and/or otherwise combined with a plurality of learning networks  621 - 623  to form a larger learning network. The combination of networks  620 - 623  can be used to further refine responses to inputs and/or allocate networks  620 - 623  to various aspects of a system, for example. 
     In some examples, in operation, “weak” connections and nodes can initially be set to zero. The learning network  620  then processes its nodes in a retaining process. In certain examples, the nodes and connections that were set to zero are not allowed to change during the retraining. Given the redundancy present in the network  620 , it is highly likely that equally good images will be generated. As illustrated in  FIG. 6B , after retraining, the learning network  620  becomes DLN  621 . The learning network  621  is also examined to identify weak connections and nodes and set them to zero. This further retrained network is learning network  622 . The example learning network  622  includes the “zeros” in learning network  621  and the new set of nodes and connections. The learning network  622  continues to repeat the processing until a good image quality is reached at a learning network  623 , which is referred to as a “minimum viable net (MVN)”. The learning network  623  is a MVN because if additional connections or nodes are attempted to be set to zero in learning network  623 , image quality can suffer. 
     Once the MVN has been obtained with the learning network  623 , “zero” regions (e.g., dark irregular regions in a graph) are mapped to the input  610 . Each dark zone is likely to map to one or a set of parameters in the input space. For example, one of the zero regions may be linked to the number of views and number of channels in the raw data. Since redundancy in the network  623  corresponding to these parameters can be reduced, there is a highly likelihood that the input data can be reduced and generate equally good output. To reduce input data, new sets of raw data that correspond to the reduced parameters are obtained and run through the learning network  621 . The network  620 - 623  may or may not be simplified, but one or more of the learning networks  620 - 623  is processed until a “minimum viable input (MVI)” of raw data input  610  is reached. At the MVI, a further reduction in the input raw data  610  may result in reduced image  630  quality. The MVI can result in reduced complexity in data acquisition, less demand on system components, reduced stress on patients (e.g., less breath-hold or contrast), and/or reduced dose to patients, for example. 
     By forcing some of the connections and nodes in the learning networks  620 - 623  to zero, the network  620 - 623  to build “collaterals” to compensate. In the process, insight into the topology of the learning network  620 - 623  is obtained. Note that network  621  and network  622 , for example, have different topology since some nodes and/or connections have been forced to zero. This process of effectively removing connections and nodes from the network extends beyond “deep learning” and can be referred to as “deep-deep learning”, for example. 
     In certain examples, input data processing and deep learning stages can be implemented as separate systems. However, as separate systems, neither module may be aware of a larger input feature evaluation loop to select input parameters of interest/importance. Since input data processing selection matters to produce high-quality outputs, feedback from deep learning systems can be used to perform input parameter selection optimization or improvement via a model. Rather than scanning over an entire set of input parameters to create raw data (e.g., which is brute force and can be expensive), a variation of active learning can be implemented. Using this variation of active learning, a starting parameter space can be determined to produce desired or “best” results in a model. Parameter values can then be randomly decreased to generate raw inputs that decrease the quality of results while still maintaining an acceptable range or threshold of quality and reducing runtime by processing inputs that have little effect on the model&#39;s quality. 
       FIG. 7  illustrates example training and deployment phases of a learning network, such as a deep learning or other machine learning network. As shown in the example of  FIG. 7 , in the training phase, a set of inputs  702  is provided to a network  704  for processing. In this example, the set of inputs  702  can include facial features of an image to be identified. The network  704  processes the input  702  in a forward direction  706  to associate data elements and identify patterns. The network  704  determines that the input  702  represents a lung nodule  708 . In training, the network result  708  is compared  710  to a known outcome  712 . In this example, the known outcome  712  is a frontal chest (e.g., the input data set  702  represents a frontal chest identification, not a lung nodule). Since the determination  708  of the network  704  does not match  710  the known outcome  712 , an error  714  is generated. The error  714  triggers an analysis of the known outcome  712  and associated data  702  in reverse along a backward pass  716  through the network  704 . Thus, the training network  704  learns from forward  706  and backward  716  passes with data  702 ,  712  through the network  704 . 
     Once the comparison of network output  708  to known output  712  matches  710  according to a certain criterion or threshold (e.g., matches n times, matches greater than x percent, etc.), the training network  704  can be used to generate a network for deployment with an external system. Once deployed, a single input  720  is provided to a deployed learning network  722  to generate an output  724 . In this case, based on the training network  704 , the deployed network  722  determines that the input  720  is an image of a frontal chest  724 . 
       FIG. 8  illustrates an example product leveraging a trained network package to provide a deep and/or other machine learning product offering. As shown in the example of  FIG. 8 , an input  810  (e.g., raw data) is provided for preprocessing  820 . For example, the raw input data  810  is preprocessed  820  to check format, completeness, etc. Once the data  810  has been preprocessed  820 , patches are created  830  of the data. For example, patches or portions or “chunks” of data are created  830  with a certain size and format for processing. The patches are then fed into a trained network  840  for processing. Based on learned patterns, nodes, and connections, the trained network  840  determines outputs based on the input patches. The outputs are assembled  850  (e.g., combined and/or otherwise grouped together to generate a usable output, etc.). The output is then displayed  860  and/or otherwise output to a user (e.g., a human user, a clinical system, an imaging modality, a data storage (e.g., cloud storage, local storage, edge device, etc.), etc.). 
     As discussed above, learning networks can be packaged as devices for training, deployment, and application to a variety of systems.  FIGS. 9A-9C  illustrate various learning device configurations. For example,  FIG. 9A  shows a general learning device  900 . The example device  900  includes an input definition  910 , a learning network model  920 , and an output definition  930 . The input definition  910  can include one or more inputs translating into one or more outputs  930  via the network  920 . 
       FIG. 9B  shows an example training device  901 . That is, the training device  901  is an example of the device  900  configured as a training learning network device. In the example of  FIG. 9B , a plurality of training inputs  911  are provided to a network  921  to develop connections in the network  921  and provide an output to be evaluated by an output evaluator  931 . Feedback is then provided by the output evaluator  931  into the network  921  to further develop (e.g., train) the network  921 . Additional input  911  can be provided to the network  921  until the output evaluator  931  determines that the network  921  is trained (e.g., the output has satisfied a known correlation of input to output according to a certain threshold, margin of error, etc.). 
       FIG. 9C  depicts an example deployed device  903 . Once the training device  901  has learned to a requisite level, the training device  901  can be deployed for use. While the training device  901  processes multiple inputs to learn, the deployed device  903  processes a single input to determine an output, for example. As shown in the example of  FIG. 9C , the deployed device  903  includes an input definition  913 , a trained network  923 , and an output definition  933 . The trained network  923  can be generated from the network  921  once the network  921  has been sufficiently trained, for example. The deployed device  903  receives a system input  913  and processes the input  913  via the network  923  to generate an output  933 , which can then be used by a system with which the deployed device  903  has been associated, for example. 
     Example Image Processing Systems and Methods to Determine Radiological Findings 
       FIG. 10  illustrates an example image processing system or apparatus  1000  including an imaging system  1010  having a processor  1020  to process image data stored in a memory  1030 . As shown in the example of  FIG. 10 , the example processor  1020  includes an image quality checker  1022 , a pre-processor  1024 , a learning network  1026 , and an image enhancer  1028  providing information to an output  1030 . Image data acquired from a patient by the imaging system  1010  can be stored in an image data store  1035  of the memory  1030 , and such data can be retrieved and processed by the processor  1020 . 
     Radiologist worklists are prioritized by putting stat images first, followed by images in order from oldest to newest, for example. By practice, most intensive care unit (ICU) chest x-rays are ordered as STAT. Since so many images are ordered as STAT, a radiologist can be unaware, among all the STAT images, which ones are really the most critical. In a large US healthcare institution, for example, a STAT x-ray order from the emergency room (ER) is typically prioritized to be read by radiologists first and is expected to be read/reported in approximately one hour. Other STAT x-ray orders, such as those acquired in the ICU, are typically prioritized next such that they may take two to four hours to be read and reported. Standard x-ray orders are typically expected to be read/reported within one radiologist shift (e.g., 6-8 hours, etc.). 
     Often, if there is an overnight radiologist (e.g., in larger healthcare facilities, etc.), the overnight radiologist is dedicated to reading advanced imaging exams (e.g., CT, MR, etc.), and only will read x-rays if there is a special request. Morning chest x-ray rounds commonly occur every day in the ICU, very early in the morning (e.g., 5 am, etc.). A daytime radiologist shift, however, may not start until 8 am. Then, the radiologist will sit and read through all the morning round images. If there is a critical finding (e.g., a patient health result that warrants immediate action such as a tension pneumothorax, mispositioned tube, impending ruptured aneurysm, etc.), the radiologist may not find it for several hours after the image was taken. In certain examples, an urgent finding, such as an aneurysm at risk of bursting imminently, etc., is distinguished from a critical finding that is important but at risk of happening later in time such as a suspicious mass that could become malignant, etc. 
     Additionally, when a tube or line is placed within a patient, it is standard practice to take an x-ray to verify correct placement of the tube or line. Due to the delay in radiologist read/reporting, clinical care teams (e.g., nurse, intensivists, etc.) may read the chest x-ray image(s) themselves to determine if any intervention is needed (e.g., medication changes to manage fluid in the lungs, adjustment of a misplaced line/tube, or confirmation of correctly place tube so they can turn on the breathing machine or feeding tube, etc.). Depending on the clinical care team&#39;s experience, skill, or attention to detail, they may miss critical findings that compromise the patient&#39;s health by delaying diagnosis, for example. When a radiologist finds a critical finding in an x-ray, the standard practice is for them to physically call the ordering physician and discuss the finding. In some cases, the ordering physician confirms they are aware and saw the issue themselves; in other cases, it is the first time they are hearing the news and will need to quickly intervene to help the patient. 
     Thus, to improve image availability, system flexibility, diagnosis time, reaction time for treatment, and the like, certain examples provide an on-device/point-of-care notification of clinical finding such as to tell a clinical team at the point of care (e.g., at a patient&#39;s bedside, etc.) to review an image as the image has a high likelihood of including a critical finding. For images with critical findings, when the image is pushed to storage such as a PACS, an HL7 message can also be sent to an associated PACS/radiology information system (RIS) and/or DICOM tag, which indicates a critical finding. A hospital information system can then create/configure rules to prioritize the radiologist worklist based on this information, for example. 
     Turning to the example of  FIG. 10 , the image quality checker  1022  processes the retrieved image data to evaluate the quality of the image data according to one or more image quality measures to help ensure that the image is of sufficient quality (e.g., good quality, other expected quality, etc.) for automated (e.g., machine learning, deep learning, and/or other artificial intelligence, etc.) processing of the image data. Image data failing to pass a quality check with respect to one or more image quality measures can be rejected as of insufficient quality, with a notification generated to alert a technologist and/or other user of the quality control failure. In certain examples, artificial intelligence (AI) can be applied to analyze the image data to evaluate image quality. 
     By hosting an AI algorithm on the imaging device  1010 , a “quality check AI” algorithm can be executed before a “critical condition AI” to help ensure that the image is of good quality/expected quality for the “critical condition AI” to perform well. The “quality check AI” can be used on the device as an assistant to the technologist (“Tech”) such as when the tech performs Quality Assurance (QA)/Quality Check (QC) practices on the images they acquire. For example, after each image is acquired, the Tech may review the image to ensure proper patient positioning, collimation, exposure/technique, no patient jewelry or clothing obstructions, no artifacts, etc. If the Tech believes the image is of good quality, then the Tech will “accept” the image. However, if the image fails the QC check, the Tech can “reject” the image and “retake” the image (e.g., re-obtain the image data through a subsequent image acquisition). 
     Depending on the Tech&#39;s experience and skill, the Tech may have a different tolerance for accept/reject image quality. However, using AI embedded in the device  1010  allows the device  1010  processor  1020  to evaluate and notify the Tech if the image fails the “quality check AI”. The image fails the quality check AI, for example, if the image is of too poor quality to reliably run through a “critical condition AI” algorithm, but simultaneously, also indicating to the Tech that perhaps the image should fail their manual/traditional QC activity as well, and that the Tech should consider a “retake”. Thus, the image quality checker  1022  can provide feedback in real-time (or substantially real-time given image data processing, transmission, and/or storage/retrieval latency) such as at the patient bedside via the output  1030  of the mobile x-ray system  200 ,  1010  indicating/recommending that an image should be re-acquired, for example. 
     Thus, rather than relying on a Tech&#39;s manual assessment, the quality checker  1022  can leverage AI and/or other processing to analyze image anatomy, orientation/position, sufficient contrast, appropriate dose, too much noise/artifacts, etc., to evaluate image quality and sufficiency to enable further automated analysis. 
     If image quality is sufficient and/or otherwise appropriate (e.g., correct view/position, correct anatomy, acceptable contrast and/or noise level, etc.) for analysis, then the pre-processor  1024  processes the image data and prepares the image data for clinical analysis. For example, the image data can be conditioned for processing by machine learning, such as a deep learning network, etc., to identify one or more features of interest in the image data. The pre-processor  1024  can apply techniques such as image segmentation to identify and divide different regions or areas in the image, for example. The pre-processor  1024  can apply techniques such as cropping to select a certain region of interest in the image for further processing and analysis, for example. The pre-processor  1024  can apply techniques such as down-sampling to scale or reduce image data size for further processing (e.g., by presenting the learning network  1026  with fewer samples representing the image data, etc.), for example. 
     The pre-processed image data is provided to the learning network  1026  for processing of the image data to identify one or more clinical/critical findings. As discussed above, the learning network  1026 , such as a deep learning network, other CNN, and/or other machine learning network, etc., receives the pre-processed image data at its input nodes and evaluates the image data according to the nodes and connective pathways of the learning network  1026  to correlate features identified in the pre-processed image data with critical and/or other clinical findings. Based on image intensity values, reference coordinate position, proximity, and/or other characteristics, items determined in the image data can be correlated with likely critical and/or other clinical findings such as a severe pneumothorax, tube within the right mainstem, free air in the bowel, etc. 
     For example, a large, highly curated set of X-Ray images can be used to train a deep convolution network (e.g., the example network of  FIGS. 3-5 , etc.) including several layers in an offline compute-intensive environment. The network is trained to output classification labels depicting a detected pathology and are able to extract features that can localize and bound regions interest to the detected pathology. The specialized network is developed and trained to output quantification metrics such as fluid density, opacity and volumetric measurements, etc. As shown in the example of  FIGS. 6A-9C , trained model(s) are deployed onto an X-Ray device (e.g., the imaging device  10 ,  200 ,  1010 , etc.) which is either mobile or installed in a fixed X-Ray room. The processor  1020  leverages the trained, deployed model(s) to infer properties, features, and/or other aspects of the image data by inputting the X-Ray image into the trained network model(s). The deployed model(s) help check quality and suitability of the image for inference via the image quality checker  1022  and infer findings via the learning network  1026 , for example. The images can be pre-processed in real time based on acquisition conditions that generated the image to improve accuracy and efficacy of the inference process. In certain examples, the learning network(s)  1026  are trained, updated, and redeployed continuously and/or periodically upon acquisition of additional curated data. As a result, more accurate and feature enhanced networks are deployed on the imaging device  1010 . 
     In certain examples, a probability and/or confidence indicator or score can be associated with the indication of critical and/or other clinical finding(s), a confidence associated with the finding, a location of the finding, a severity of the finding, a size of the finding, and/or an appearance of the finding in conjunction with another finding or in the absence of another finding, etc. For example, a strength of correlation or connection in the learning network  1026  can translate into a percentage or numerical score indicating a probability of correct detection/diagnosis of the finding in the image data, a confidence in the identification of the finding, etc. 
     The image data and associated finding(s) can be provided via the output  1030  to be displayed, reported, logged, and/or otherwise used in a notification or alert to a healthcare practitioner such as a Tech, nurse, intensivist, trauma surgeon, etc., to act quickly on the critical and/or other clinical finding. In some examples, the probability and/or confidence score, and/or a criticality index/score associated with the type of finding, size of finding, location of finding, etc., can be used to determine a severity, degree, and/or other escalation of the alert/notification to the healthcare provider. For example, certain detected conditions result in a text-based alert to a provider to prompt the provider for closer review. Other, more serious conditions result in an audible and/or visual alert to one or more providers for more immediate action. Alert(s) and/or other notification(s) can escalate in proportion to an immediacy and/or other severity of a probable detected condition, for example. 
     Image data and associated finding(s) can be provided to image enhancer  1028  for image post-processing to enhance the image data. For example, the image enhancer  1028  can process the image data based on the finding(s) to accentuate the finding(s) in a resulting image. Thus, when the enhanced image data is provided to the output  1030  for display (e.g., via one or more devices such as a mobile device  1040 , display  1042 , PACS and/or other information system  1044 , etc.), the finding(s) are emphasized, highlighted, noted, and/or otherwise enhanced in the resulting displayed image, for example. 
     By running AI on the imaging device  1010 , AI findings can be leveraged to conduct enhanced image processing. For example, if the AI detects tubes/lines present in the image data, then the device software can process the image using an image processing technique best for viewing tubes/lines. For example, tubes and/or other lines (e.g., catheter, feeding tube, nasogastric (NG) tube, endotracheal (ET) tube, chest tube, pacemaker leads, etc.) can be emphasized or enhanced in the image data through an image processing algorithm that decomposes the image data into a set of spatial frequency bands. Non-linear functions can be applied to the frequency bands to enhance contrast and reduce noise in each band. Spatial frequencies including tubes and lines are enhanced while spatial frequencies including noise are suppressed. As a result, the tubes and lines are more pronounced in a resulting image. Similarly, a pneumothorax (e.g., an abnormal collection of air in pleural space between a lung and the chest), fracture, other foreign object, etc., representing a finding can be emphasized and/or otherwise enhanced in a resulting image, for example. 
     The enhanced image data and associated finding(s) can be output for display, storage, referral, further processing, provision to a computer-aided diagnosis (CAD) system, etc., via the output  1030 . The output  1030  can provide information to a plurality of connected devices  1040 - 1044  for review, storage, relay, and/or further action, for example. 
     The more contextual information available about a patient, the more informed, accurate, and timely diagnosis a physician can make. Similarly, the more information provided to an AI algorithm model, the more accurate the prediction generated by the model. As described above, AI models can be used to deploy algorithms on an imaging device to provide bedside, real-time point of care notifications when a patient has a critical finding, without suffering form latency or connectivity risks from running an AI model remotely. However, contextual data is not readily available on the imaging device, so certain examples retrieve and use contextual patient data for AI algorithm input on the imaging device. 
     Contextual patient information can be used to improve accuracy of an artificial intelligence algorithm model, for example. For example, a pneumothorax, or collapsed lung, is more common in tall, thin men and people with a prior history of the condition. Therefore, a chest x-ray pneumothorax AI detection algorithm, when provided electronic medical record information regarding patient body type and patient medical history, can more accurately predict the presence of the pneumothorax disease. 
     Additionally, contextual patient information can be used to determine whether a clinical condition is worsening, improving, or staying the same over time, for example. For example, a critical test result from a chest x-ray exam is considered to be a “new or significant progression of pneumothorax”, in which the radiologist shall call the ordering practitioner and verbally discuss the findings. Although, chest x-ray pneumothorax AI detection algorithm that resides on the mobile x-ray system, may not have the patient&#39;s prior chest x-ray to determine whether a pneumothorax finding is new or significantly progressed. Therefore, providing the AI algorithm with prior imaging exams, would be necessary to determine whether the pneumothorax finding shall be considered critical or not. 
     Some examples of contextual patient data, which can be used by artificial intelligence for detection, classification, segmentation, etc., include: electronic medical record data (e.g., age, gender, weight, height, body mass index (BMI), smoking status, medication list, existing conditions, prior conditions, etc.), prior images (e.g., x-ray, CT, MR, etc.), electrocardiogram (EKG) heart monitor data, patient temperature, oxygen saturation/O2 meter data, blood pressure information, ventilator metrics (e.g., respiratory rate, etc.), fluid and medication administration data (e.g., intravenous (IV) fluid administration, etc.), etc. For example, electronic medical record data can be retrieved using an HL7 query retrieve message, prior images can be retrieved using a PACS query, data from wireless patient monitoring devices (e.g., an EKG heart monitor, etc.) can be retrieved using wireless connectivity (e.g., Wi-Fi, etc.) between devices, etc. 
     Thus, contextual patient data can be collected on an imaging device to enable more accurate and advanced artificial intelligence algorithms. In certain examples, change versus no change “AI algorithms can be modeled on an imaging device to detect progression of disease. Leveraging more/diverse data sources allows the system to create higher-performing AI algorithms, for example. In certain examples, an AI algorithm model can be implemented on the imaging device, on an edge server, and/or on a cloud-based server where other contextual data is collected, for example. 
     While certain examples generate and apply an AI algorithm model using a current imaging exam as input, other examples pull additional data from sources off the imaging device to form input for an AI model. 
     While example implementations are illustrated in conjunction with  FIGS. 1-10 , elements, processes and/or devices illustrated in conjunction with  FIGS. 1-10  can be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, components disclosed and described herein can be implemented by hardware, machine readable instructions, software, firmware and/or any combination of hardware, machine readable instructions, software and/or firmware. Thus, for example, components disclosed and described herein can be implemented by analog and/or digital circuit(s), logic circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the components is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. 
     Flowcharts representative of example machine readable instructions for implementing components disclosed and described herein are shown in conjunction with at least  FIGS. 11-12 and 28-29 . In the examples, the machine readable instructions include a program for execution by a processor such as the processor  3012  shown in the example processor platform  3000  discussed below in connection with  FIG. 30 . The program may be embodied in machine readable instructions stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor  3012 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  3012  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowcharts illustrated in conjunction with at least  FIGS. 11-12 and 28-29 , many other methods of implementing the components disclosed and described herein may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Although the flowcharts of at least  FIGS. 11-12 and 28-29  depict example operations in an illustrated order, these operations are not exhaustive and are not limited to the illustrated order. In addition, various changes and modifications may be made by one skilled in the art within the spirit and scope of the disclosure. For example, blocks illustrated in the flowchart may be performed in an alternative order or may be performed in parallel. 
     As mentioned above, the example processes of at least  FIGS. 11-12 and 28-29  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example processes of at least  FIGS. 11-12 and 28-29  can be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. In addition, the term “including” is open-ended in the same manner as the term “comprising” is open-ended. 
     As shown in the example method  1100  depicted in  FIG. 11 , acquired image data can be analyzed by the imaging device  1010  at the location of image acquisition (e.g., patent bedside, imaging room, etc.) to evaluate image quality and identify likely critical and/or other clinical findings to trigger further image and patient review and action. At block  1110 , acquired image data acquired by the device  1010  is processed by the device  1010  to evaluate the quality of the image data to help ensure that the image is of sufficient quality (e.g., good quality, other expected quality, etc.) for automated (e.g., machine learning, deep learning, and/or other artificial intelligence, etc.) processing of the image data. Image data failing to pass a quality check can be rejected as of insufficient quality, with a notification generated to alert a technologist and/or other user of the quality control failure. In certain examples, artificial intelligence (AI) can be applied by the image quality checker  1022  to analyze the image data to evaluate image quality. 
     By hosting an AI algorithm on the imaging device  1010 , a “quality check AI” algorithm can be executed before a “critical condition AI” to help ensure that the image is of good quality/expected quality for the “critical condition AI” to perform well. The “quality check AI” can be used on the device as an assistant to the technologist (“Tech”) such as when the tech performs Quality Assurance (QA)/Quality Check (QC) practices on the images they acquire. Using AI embedded in the device  1010  allows the device  1010  processor  1020  to evaluate and notify  1115  the Tech if the image fails the “quality check AI”. The image fails the quality check AI, for example, if the image is of too poor quality to reliably run through a “critical condition AI” algorithm, but simultaneously, also indicating to the Tech that perhaps the image should fail their manual/traditional QC activity as well, and that the Tech should consider a “retake”. Thus, the image quality checker  1022  can provide feedback in real-time (or substantially real-time given image data processing, transmission, and/or storage/retrieval latency) such as at the patient bedside via the output  1030  of the mobile x-ray system  200 ,  1010  indicating/recommending via a notification  1115  that an image should be re-acquired, for example. For example, the notification  1115  can be provided via an overlay on the mobile device  1040 , display  1042 , etc., to show localization (e.g., via a heatmap, etc.) of the AI finding and/or associated information. 
     Thus, rather than relying on a Tech&#39;s manual assessment, the quality checker  1022  can leverage AI and/or other processing to analyze image anatomy, orientation/position, sufficient contrast, appropriate dose, too much noise/artifacts, etc., to evaluate image quality and sufficiency to enable further automated analysis. 
     If image quality is sufficient and/or otherwise appropriate (e.g., correct view/position, correct anatomy, acceptable patient positioning, contrast and/or noise level, etc.) for analysis, then, at block  1120 , the image data is pre-processed to prepare the image data for clinical analysis. For example, the image data can be conditioned for processing by machine learning, such as a deep learning network, etc., to identify one or more features of interest in the image data. The pre-processor  1024  can apply techniques such as image segmentation to identify and divide different regions or areas in the image, for example. The pre-processor  1024  can apply techniques such as cropping to select a certain region of interest in the image for further processing and analysis, for example. The pre-processor  1024  can apply techniques such as down-sampling, anatomical segmentation, normalizing with mean and/or standard deviation of training population, contrast enhancement, etc., to scale or reduce image data size for further processing (e.g., by presenting the learning network  1026  with fewer samples representing the image data, etc.), for example. 
     At block  1130 , the pre-processed image data is provided to the learning network  1026  for processing of the image data to identify one or more clinical/critical findings. As discussed above, the learning network  1026 , such as a deep learning network, other CNN and/or other machine learning network, etc., receives the pre-processed image data at its input nodes and evaluates the image data according to the nodes and connective pathways of the learning network  1026  to correlate features identified in the pre-processed image data with critical and/or other clinical findings. Based on image intensity values, reference coordinate position, proximity, and/or other characteristics, items determined in the image data can be correlated with likely critical and/or other clinical findings such as a severe pneumothorax, tube within the right mainstem, free air in the bowel, etc. 
     For example, a large, highly curated set of X-Ray images can be used to train a deep convolution network (e.g., the example network of  FIGS. 3-5 , etc.) including several layers in an offline compute-intensive environment. The network is trained to output classification labels depicting a detected pathology and are able to extract features that can localize and bound regions interest to the detected pathology. The specialized network is developed and trained to output quantification metrics such as fluid density, opacity and volumetric measurements, etc. As shown in the example of  FIGS. 6A-9C , trained model(s) are deployed onto an X-Ray device (e.g., the imaging device  10 ,  200 ,  1010 , etc.) which is either mobile or installed in a fixed X-Ray room. The processor  1020  leverages the trained, deployed model(s) to infer properties, features, and/or other aspects of the image data by inputting the X-Ray image into the trained network model(s). The deployed model(s) help check quality and suitability of the image for inference via the image quality checker  1022  and infer findings via the learning network  1026 , for example. The images can be pre-processed in real time based on acquisition conditions that generated the image to improve accuracy and efficacy of the inference process. In certain examples, the learning network(s)  1026  are trained, updated, and redeployed continuously and/or periodically upon acquisition of additional curated data. As a result, more accurate and feature enhanced networks are deployed on the imaging device  1010 . 
     In certain examples, a probability and/or confidence indicator or score can be associated with the indication of critical and/or other clinical finding(s), as well as a size of the finding, location of the finding, severity of the finding, etc. For example, a strength of correlation or connection in the learning network  1026  can translate into a percentage or numerical score indicating a probability of correct detection/diagnosis of the finding in the image data, a confidence in the identification of the finding, etc. 
     The image data and associated finding(s) can be provided via the output  1030  to be displayed, reported, logged, and/or otherwise used in a notification or alert  1135  to a healthcare practitioner such as a Tech, nurse, intensivist, trauma surgeon, and/or clinical system, etc., to act quickly on the critical and/or other clinical finding. In some examples, the probability and/or confidence score, and/or a criticality index/score associated with the type of finding, can be used to determine a severity, degree, and/or other escalation of the alert/notification to the healthcare provider. For example, certain detected conditions result in a text-based alert to a provider to prompt the provider for closer review. Other, more serious conditions result in an audible and/or visual alert to one or more providers for more immediate action. Alert(s) and/or other notification(s) can escalate in proportion to an immediacy and/or other severity of a probable detected condition, for example. 
     At block  1140 , image data is enhanced based on associated finding(s) identified by the learning network  1026 . For example, the image enhancer  1028  can process the image data based on the finding(s) to accentuate the finding(s) in a resulting image. Thus, when the enhanced image data is provided to the output  1030  for display (e.g., via one or more devices such as a mobile device  1040 , display  1042 , PACS and/or other information system  1044 , etc.), the finding(s) are emphasized, highlighted, noted, and/or otherwise enhanced in the resulting displayed image, for example. 
     By running AI on the imaging device  1010 , AI findings can be leveraged to conduct enhanced image processing. For example, if the AI detects tubes/lines present in the image data, then the device software can process the image using an image processing technique best for viewing tubes/lines. 
     The enhanced image data and associated finding(s) can be output for display, storage, referral, further processing, provision to a computer-aided diagnosis (CAD) system, etc., via the output  1030 . The output  1030  can provide information to a plurality of connected devices  1040 - 1044  for review, storage, relay, and/or further action, for example. As shown in the example of  FIG. 11 , enhanced image data and associated finding(s) can be output for display on a device  1150  (e.g., a handheld or mobile device, etc.), displayed on a workstation  1152  (e.g., an information system, a display associated with the imaging device  1010 , etc.), and/or sent to a clinical information system such as a PACS, RIS, enterprise archive, etc., for storage and/or further processing  1154 . 
       FIG. 12  illustrates a flow diagram for example implementation of checking image quality ( 1110 ) and applying artificial intelligence ( 1130 ) to image data to determine critical and/or other clinical findings in the image data. 
     Portable, real-time, at point of patient care, at point of image acquisition, dynamic determination and prompting for further action, integrated into imaging device. At  1202 , image data, such as DICOM image data, is provided from a mobile x-ray imaging device (e.g., the device  200  and/or  1010 , etc.). At  1204 , metadata associated with the image data (e.g., DICOM header information, other associated metadata, etc.) is analyzed to determine whether the image data matches a position and region indicated by the metadata. For example, if the DICOM metadata indicates that the image is a frontal (e.g., anteroposterior (AP) or posteroanterior (PA)) chest image, then an analysis of the image data should confirm that position (e.g., location and orientation, etc.). If the image does not match its indicated position and region, then, at  1206 , a notification, alert, and/or warning is generated indicating that the image is potentially improper. The warning can be an audible, visual, and/or system alert or other notification, for example, and can prompt a user for further action (e.g., re-acquire the image data, etc.), trigger a system for further action, log the potential error, etc. 
     If the image data appears to match its prescribed position and region, then, at  1208 , the image data is analyzed to determine whether the image passes image quality control check(s). For example, the image data is analyzed to determine whether the associated image has good patient positioning (e.g., the patient is positioned such that an anatomy or region of interested is centered in the image, etc.). Other quality control checks can include an evaluation of sufficient contrast, an analysis of a level of noise or artifact in the image, an examination of appropriate/sufficient dosage for image clarity, etc. 
     If the image fails a quality control check, then, at  1210 , a warning of compromised image quality is generated. For example, a user, other system, etc., can receive an alert and/or other notification (e.g., a visual and/or audible alert on screen, via message, log notation, trigger, etc.) that the image quality may not be sufficient and/or may present issues in evaluating the image data to determine clinical finding(s). At  1212 , settings and/or other input is evaluated to determine whether to proceed with further image processing. For example, user input in response to the notification can indicate whether or not to proceed anyway, and/or a configuration setting, etc., can specify a default instruction or threshold regarding whether or not to proceed with further image analysis despite image quality concerns. If the instruction is not to proceed, then the process  1200  ends. 
     If analysis is to proceed (e.g., because the image passes quality check(s) and/or an instruction indicates to proceed despite image quality concerns, etc.), then, at  1214 , the image data is evaluated with respect to a clinical check. For example, a deep learning network, machine learning, and/or other AI is applied to analyze the image data to detect the presence of a critical and/or other clinical finding. For example, image data can be processed by the learning network  1026  to identify a severe pneumothorax and/or other condition (e.g., tube within the right mainstem, free air in the bowel, fracture, tumor, lesion, other foreign object, etc.) in the image data. If no finding is determined, then the process  1200  ends. 
     If, however, a finding is determined, then, at  1216 , a finding alert and/or other notification is generated. For example, a critical finding alert is generated based on the identification of a pneumothorax, incorrect position of an ET tube, position of tube in right main stem, etc. The alert can be generated in proportion to and/or other correlation with a severity/urgency of the clinical finding, confidence in the finding, type of finding, location of the finding, and/or appearance of the finding in conjunction with another finding or in absence of another finding, for example. For example, a critical finding can be alerted more urgently to a healthcare practitioner and/or other user than a less-critical clinical finding. On-screen alert(s) can be 13-displayed, HL7 messages can be provided to the RIS, etc. In certain examples, image data can be re-processed such as by the image enhancer  1028  to more optimally display the finding(s) to a user. 
       FIGS. 13-20  illustrate example displays to provide output and facilitate interaction including on-screen alerts, indicators, notifications, etc., in accordance with the apparatus, systems, and methods described above in connection with  FIGS. 1-12 . The example displays can be provided via the imaging devices  10 ,  200 ,  1010 , and/or a separate handheld or mobile computing device, workstation, etc. 
       FIG. 13  shows an example graphical user interface (GUI)  1300  including a deviation index (DI)  1310  (e.g., an indication of correct image acquisition technique with 0.0 being a perfect exposure) and an indication of priority  1320  (e.g., from processing by the system  1000  including AI). As shown in the example of  FIG. 13 , the priority indication  1320  is high  1322 .  FIG. 14  illustrates the example GUI  1300  with a priority indication  1320  of medium  1324 .  FIG. 15  illustrates the example GUI  1300  with a priority indication  1310  of low  1326 . 
       FIG. 16  illustrates an example GUI  1600  including a DI  1610 , a quality control indicator  1620  (e.g., pass or fail for acceptable quality, etc.), a criticality index  1630  (e.g., normal, abnormal, critical, etc.), a criticality value  1635  associated with the criticality index  1630 , an indication of finding  1640  (e.g., mass, fracture, pneumothorax, etc.), and an indication of size or severity  1650  (e.g., small, medium, large, etc.). Thus, a user can interact with the example GUI  1600  and evaluate the DI  1610 , quality indication  1620 , criticality range  1630  and value  1635  for clinical impression, type of finding  1640 , and severity of finding  1650 . 
       FIG. 17  illustrates another example GUI  1700 , similar to but reduced from the example GUI  1600 , including a DI  1710 , a criticality impression  1720 , an indication of finding  1730 , and an indication of severity  1740 .  FIG. 18  illustrates an example GUI  1800  similar to the example GUI  1700  further including an overlay  1810  of the finding on the image. 
       FIG. 19  illustrates an example GUI  1900  providing potential findings from AI in a window  1910  overlaid on the image viewer display of the GUI  1900 .  FIG. 20  illustrates another view of the example GUI  1900  in which entries  2002 ,  2004  in the AI findings window  1910  have been expanded to reveal further information regarding the respective finding  2002 ,  2004 .  FIG. 21  illustrates a further view of the example GUI  1900  in which the AI findings window  1910  has been reduced to a miniature representation  2110 , selectable to view information regarding the findings. 
       FIG. 22  illustrates an example GUI  2200  in which a finding  2210  is highlighted on an associated image, and related information  2220  is also displayed.  FIG. 23  illustrates an example configuration interface  2300  to configure AI to process image and/or other data and generate findings. 
       FIG. 24  illustrates a first example abbreviated GUI  2400  (e.g., a web-based GUI, etc.) displayable on a smartphone  2410  and/or other computing device, and a second abbreviated GUI  2420  shown on a tablet  2430 . As shown in the example of  FIG. 24 , the tablet  2430  can be mounted with respect to the imaging device  10 ,  200 ,  1010  for viewing and interaction by an x-ray technician and/or other healthcare practitioner, for example. 
     In certain examples, an exam can be prioritized on a worklist based on the evaluation of the exam and detection of a critical finding. For example, a message can be sent from the imaging modality (e.g., a GE Optima™ XR240 x-ray system, GE LOGIQ™ ultrasound system, other mobile imaging system, etc.) to an information system (e.g., a RIS, PACS, etc.) to elevate an image on the worklist when a critical finding is detected. 
       FIG. 25  illustrates an example system configuration  2500  in which the imaging system  1010  interfaces with a broker device  2510  to communicate with the PACS  1044 , a RIS  2520 , and/or other health information system, for example. As shown in the example of  FIG. 25 , the RIS  2520  provides a modality worklist (MWL) to the imaging device  1010 . The MWL can be provided as a service-object pair (SOP), for example, to enable the imaging system  1010  to query for patient demographics and study details from an MWL service class provider, such as the RIS  2520 . For example, the imaging system  1010  can query the RIS  2520  for a list of patients satisfying a criterion(-ia), and the RIS  2520  responds with results. 
     As shown in the example of  FIG. 25 , the RIS  2520  provides the MWL SOP to the imaging device  1010 , and the broker  2510  (e.g., an AI/HL7/DICOM broker, etc.) facilitates a query by the imaging device  1010  and response by the RIS  2520 . The imaging device  1010  can also use the broker  2510  to send a message to the RIS  2520  to move up an exam on the worklist when a critical finding is detected, which results in an updated MWL provided from the RIS  2520  to the imaging device  1010 . Messages can be exchanged via the broker  2510  as a cross-platform HL7 and/or DICOM interface engine sending bi-directional HL7/DICOM messages between systems and/or applications running on the systems over multiple transports, for example. 
     Output from the imaging device  1010  can be stored on the PACS  1044  and/or other device (e.g., an enterprise archive, vendor neutral archive, other data store, etc.). For example, a DICOM storage SOP can be used to transfer images, alerts, and/or other data from the imaging system  1010  to the PACS  1044 . 
     Thus, rules can be created to determine image/exam priority, and those rules can be stored such as in a DICOM header of an image sent to the PACS  1044 . An AI model can be used to set a score or a flag in the DICOM header (e.g., tag the DICOM header) to be used a rule to prioritize those exams. Thus, a DICOM header tag (e.g., reflecting the score or flag, etc.) can be used to build priority rules. For example, a flag can be used to indicate an urgent or STAT exam to be reviewed. A score can be used to assign a relative degree of priority, for example. HL7 messages can be communicated to and from the imaging device  1010  via the broker  2510  to provide prioritization instructions, as well as other structured reports, DICOM data, HL7 messages, etc. Using DICOM header and HL7 information, a client system, such as the RIS  2520 , PACS  1044 , etc., can determine priority. 
     In certain examples, prioritization rules can be made available on a cloud-based server, an edge device, and/or a local server to enable cross-modality prioritization of exams in a worklist. Thus, rather than or in addition to prioritizing based on wait time, physician, cost, etc., AI processing of image data can influence and/or dictate exam and/or follow-up priority, and the prioritization scheme can be distributed within and/or across, modalities, locations, etc., to improve outcomes, for example. AI processing provides instant notification at the imaging device  1010  as well as longer-term prioritization determining an ordering of images, exams, and/or other data for review. 
       FIG. 26  illustrates an example system configuration  2600  in which an artificial intelligent model executes on an edge device  2610  to provide point of care alerts on a vendor neutral imaging machine  1010 . The edge device  2610  can interface between local systems and a cloud-based learning “factory” or platform  2630 , for example. The edge device  2610  (e.g., a tablet and/or other handheld computing device, laptop, etc.) executes an AI model and receives DICOM images from the imaging system  1010  as well as radiology reading reports from the RIS  2520 . The edge device  2610  posts data to the cloud factory  2630  including image data and associated report(s) for AI processing, data aggregation on the cloud, etc. 
     As shown in the example of  FIG. 26 , the imaging system  1010  can also send images to the PACS  1044  for storage. The PACS  1044  and the RIS  2520  can interact to exchange information such as provide images to the RIS  2520  to allow a user at a workstation  2640  to read the image and dictate a report. 
     As shown in the example of  FIG. 26 , an AI model executing on the edge device  2610  can generate a real-time AI report  2650  for a user, other system, application, etc. Thus, the edge device  2610  can provide real-time alerts at the point of care to trigger follow-up action, for example. 
     In certain examples, such as shown in  FIG. 26 , a workstation  2660  associated with the cloud-based learning factory server and associated model(s)  2630  can receive images, reports, etc., to review and evaluate the AI&#39;s assessment of the images, etc. Feedback can be used to adjust the AI models, and can be provided to the PACS  1044 , RIS  2520 , etc., to re-annotate images for reporting, follow-up, etc. 
     Thus, the edge device  2610  can be positioned near the imaging device  1010  and be mobile to provide AI image processing and critical finding feedback, alerting, etc., while serving as an intermediary between local systems  1010 ,  1044 ,  2520 , and a remote cloud-based system  2630 . The power of the cloud-based learning factory  2630  can be used to bolster local on-device  2610  AI capabilities and deploy updated AI models to the edge device  2610  to improve processing of the data, for example. 
     In certain examples, results from previous-inferenced image(s) can be provided via the edge device  2610  to generate an AI point of care alert based on a delta or difference in inference results from currents results to the prior results. Thus, an evolution or change in results can be evaluated and used to trigger (or withdraw) a point of care alert. The edge device  2610  can retrieve prior and/or other analysis from the health cloud  2630 , for example. 
       FIG. 27  illustrates an example system  2700  to incorporate and compare AI results between current and prior exams. As shown in the example of  FIG. 27 , an AI container and/or other virtual machine (e.g., a Docker container, etc.)  2710  instantiates an AI inferencing engine which produces AI results and provides the AI results (e.g., via JSON, etc.) to form a context  2720  (e.g., an AI-augmented patient and/or exposure context, etc.). The context  2720  forms enriched AI results to provide to the broker  2510 , which conveys those results to connected systems such as the RIS  2520 , PACS  1044 , etc. The broker  2510  also processes the AI results and facilitates aggregation and querying of AI results for an AI results comparator  2740 , which also receives AI results from the AI container  2710 , for example. 
     AI results can be queried via the broker  2510  based on patient identifier (ID), exam type, imaging device, etc. The comparator  2740  generates a change notification  2750  when current AI results have diverged and/or otherwise differ from prior AI results of the image data for the patient, for example. The edge device  2610 , cloud system  2630 , and/or other recipient can receive the change notification  2750  to trigger a point of care alert and/or additional actions to follow-up on the identified change, for example. 
     As shown in the example of  FIG. 27 , the broker  2510  can include an order update channel  2732  to update an order with respect to a patient at the RIS  2520  and/or the PACS  10144 , and a database update channel  2734 . AI results can be provided to the database update channel  2734  to update an AI database  2736 , for example. A database read channel  2738  in the broker  2510  can be used to query AI results from the data store  2736  for the comparator  2740 , for example. 
       FIG. 28  illustrates a flow diagram for a method  2800  to prioritize, in a worklist, an exam related to a critical finding for review. At block  2802 , a critical finding is detected in image data at a modality  1010 . For example, an AI model running at the imaging device  1010  identifies a critical finding (e.g., presence of a pneumothorax, misplaced tube or line, stroke, etc.) prompting further review. At block  2804 , the image data is stored. For example, the image data related to the critical finding is stored at the PACS  1044 . At block  2806 , a message is sent from the modality  1010  to the RIS  2520  to adjust the worklist based on the critical finding. Thus, the imaging device  1010  can instruct the RIS  2520  to move up an exam due to the identification of a critical finding. 
       FIG. 29  illustrates a flow diagram of a method  2900  to compare current and prior AI analyses of image data to generate a notification for a point of care alert. At block  2902 , prior AI image processing results are received (e.g., at the AI Container  2710 , the broker  2510 , etc.). At block  2904 , enriched AI results are generated with patient and/or exposure context. Thus, the context  2720  enriches the result data with information regarding the patient, the image exposure, the condition, history, environment, etc. 
     At block  2906 , connected systems are updated based on the enriched AI results via the broker  2510 . For example, an exam order associated with the patient can be updated at the RIS  2520  based on the enriched AI results. Additionally, at block  2908 , a database  2734  of AI result information can be updated. At block  2910 , query results are provided to the comparator  2740 , which, at block  2912 , compares current AI results with prior AI results for the patient to determine a difference between the results. Thus, the comparator  2740  can detect a change in the AI analysis of the patient&#39;s image data. In certain examples, the comparator  2740  can indicate a direction of change, a trend in the change, etc. 
     At block  2914 , a change notification is generated by the comparator  2740  when the current and prior results differ. For example, if the current and prior AI results differ by more than a threshold amount (e.g., by more than a standard deviation or tolerance, etc.), then the change notification  2750  is generated. The change notification can prompt a point of care alert at the imaging device  1010 , associated tablet or workstation, RIS  2520  reading workstation  2640 , etc. 
     Thus, certain examples enable an AI-driven comparison between current and prior images and associated interpretations (e.g., change versus no change, worse or better, progress or not, etc.). Additionally, information such as co-morbidities, patient demographics, and/or other EMR data mining can be combined with image data to generate a risk profile. For example, information such as patient demographics, prior images, previous alert, co-morbidities, and/or current image can factor into producing an alert/no alert, increase/decrease severity of alert, etc. In an example, a patient in the intensive care unit is connected to a ventilator, an oxygen meter, a blood pressure meter, an IV drip, and/or other monitor(s) while having images obtained. Additional data from connected meter(s)/sensor(s) can be combined with the image data to allow the AI model to better interpret the image data. A higher confidence score and/or other greater degree of confidence can be assigned to an AI model prediction when more information is provided. Patient monitoring and/or other sensor information, patient vitals, etc., can be combined with prior imaging data to feed into an AI algorithm model. Prior images and/or current images can be compared and/or otherwise analyzed to predict a condition and/or otherwise identify a critical finding with respect to the patient. 
     Thus, certain examples help ensure and improve data and analysis quality. Providing an AI model on the imaging device  1010  enables immediate point-of-care action if the patient is critical or urgent. In some examples, cloud-based access allows retrieval of other images for comparison while still providing a local alert in real time at the machine  1010 . Cloud access can also allow offloading of AI functionality that would otherwise be on the edge device  2610 , machine  1010 , broker  2510 , etc. 
     In certain examples, the broker  2510  can be used to intercept an image being transmitted to the PACS  1044 , and a prioritization message can be inserted to move an associated image exam up in the worklist. 
     In certain examples, a machine learning, deep learning, and/or other artificial intelligence model can improve in quality based on information being provided to train the model, test the model, exercise the model, and provide feedback to update the model. In certain examples, AI results can be verified by reviewing whether an AI-identified anatomy in an image is the correct anatomy for the protocol being conducted with respect to the patient. Positioning in the image can also be evaluated to help ensure that organ(s)/anatomy are in view in the image that are expected for the protocol position, for example. In certain examples, a user can configure his or her own use case(s) for particular protocol(s) to be verified by the AI. For example, anatomy view and position, age, etc., can be checked and confirmed before executing the AI algorithm model to help ensure quality and clinical compliance. 
     In certain examples, a critical finding, such as a pneumothorax, is identified by the AI model in the captured image data. For example, AI results can indicate a likely pneumothorax (PTX) in the analyzed image data. In certain examples, feedback can be obtained to capture whether the user agrees with the AI alerts (e.g., select a thumbs up/down, specify manual determination, etc.). In certain examples, an audit trail is created to capture a sequence of events, actions, and/or notifications to verify timing, approval, message routing/alerting, etc. 
     In certain examples, patient context can provide a constraint on application of an AI model to the available image and/or other patient data. For example, patient age can be checked (e.g., in a DICOM header, via an HL7 message from a RIS or other health information system, etc.), and the algorithm may not be run if the patient is less than 18 years old (or a message to a user can be triggered to indicate that the algorithm may not be as reliable for patients under 18). 
     With pneumothorax, for example, air is present in the pleural space and indicates thoracic disease in the patient. Chest x-ray images can be used to identify the potential pneumothorax near a rib boundary based on texture, contour, pixel values, etc. The AI model can assign a confidence score to that identification or inference based on the strength of available information indicating the presence of the pneumothorax, for example. Feedback can be provided from users to improve the AI pneumothorax detection model, for example. 
       FIG. 30  is a block diagram of an example processor platform  3000  structured to executing the instructions of at least  FIGS. 11-12 and 28-29  to implement the example components disclosed and described herein. The processor platform  3000  can be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, or any other type of computing device. 
     The processor platform  3000  of the illustrated example includes a processor  3012 . The processor  3012  of the illustrated example is hardware. For example, the processor  3012  can be implemented by integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. 
     The processor  3012  of the illustrated example includes a local memory  3013  (e.g., a cache). The example processor  3012  of  FIG. 30  executes the instructions of at least  FIGS. 11-12 and 28-29  to implement the systems, infrastructure, displays, and associated methods of  FIGS. 1-27  such as the image quality checker  1022 , the pre-processor  1024 , the learning network  1026 , the image enhancer  1028 , the output  1030  of the processor  1020 / 3012 , the broker  2510 , the edge device  2610 , etc. The processor  3012  of the illustrated example is in communication with a main memory including a volatile memory  3014  and a non-volatile memory  3016  via a bus  3018 . The volatile memory  3014  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  3016  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  3014 ,  3016  is controlled by a clock controller. 
     The processor platform  3000  of the illustrated example also includes an interface circuit  3020 . The interface circuit  3020  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  3022  are connected to the interface circuit  3020 . The input device(s)  3022  permit(s) a user to enter data and commands into the processor  3012 . The input device(s) can be implemented by, for example, a sensor, a microphone, a camera (still or video, RGB or depth, etc.), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  3024  are also connected to the interface circuit  3020  of the illustrated example. The output devices  3024  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, and/or speakers). The interface circuit  3020  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     The interface circuit  3020  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  3026  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  3000  of the illustrated example also includes one or more mass storage devices  3028  for storing software and/or data. Examples of such mass storage devices  3028  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     The coded instructions  3032  of  FIG. 30  may be stored in the mass storage device  3028 , in the volatile memory  3014 , in the non-volatile memory  3016 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will be appreciated that the above disclosed methods, apparatus, and articles of manufacture have been disclosed to monitor, process, and improve operation of imaging and/or other healthcare systems using a plurality of deep learning and/or other machine learning techniques. 
     Thus, certain examples facilitate image acquisition and analysis at the point of care such as via a portable imaging device at the point of patient imaging. If images should be re-taken, further analysis done right away, and/or other criticality explored sooner, rather than later, the example systems, apparatus, and methods disclosed and described herein can facilitate such action to automate analysis, streamline workflow, and improve patient care. 
     Certain examples provide a specially-configured imaging apparatus that can acquire images and operate as a decision support tool at the point of care for a critical care team. Certain examples provide an imaging apparatus that functions as a medical device to provide and/or facilitate diagnosis at the point of care to detect radiological findings, etc. The apparatus can trigger a critical alert for a radiologist and/or critical care team to bring immediate attention to the patient. The apparatus enables patient triaging after the patient&#39;s exam, such as in a screening environment, wherein negative tests allow the patient to return home, while a positive test would require the patient to be seen by a physician before returning home 
     In certain examples, a mobile device and/or cloud product enables a vendor-neutral solution, proving point of care alerts on any digital x-ray system (e.g., fully integrated, upgrade kit, etc.). In certain examples, embedded AI algorithms executing on a mobile imaging system, such as a mobile x-ray machine, etc., provide point of care alerts during and/or in real-time following image acquisition, etc. 
     By hosting AI on the imaging device, the mobile x-ray system can be used in rural regions without hospital information technology networks, or even on a mobile truck that brings imaging to patient communities, for example. Additionally, if there is long latency to send an image to a server or cloud, AI on the imaging device can instead be executed and generate output back to the imaging device for further action. Rather than having the x-ray technologist moved onto the next patient and the x-ray device no longer at the patient&#39;s bedside with the clinical care team, image processing, analysis, and output can occur in real time (or substantially real time given some data transfer/retrieval, processing, and output latency) to provide a relevant notification to the clinical care team while they and the equipment are still with or near the patient. For trauma cases, for example, treatment decisions need to be made fast, and certain examples alleviate the delay found with other clinical decision support tools. 
     Mobile X-ray systems travel throughout the hospital to the patient bedside (e.g., emergency room, operating room, intensive care unit, etc. Within a hospital, network communication may be unreliable in “dead” zones of the hospital (e.g., basement, rooms with electrical signal interference or blockage, etc.). If the X-ray device relies on building Wi-Fi, for example, to push the image to a server or cloud which is hosting the AI model and then wait to receive the AI output back to the X-ray device, then patient is at risk of not having reliability in critical alerts when needed. Further, if a network or power outage impacts communications, the AI operating on the imaging device can continue to function as a self-contained, mobile processing unit. 
     Examples of alerts generated for general radiology can include critical alerts (e.g., for mobile x-ray, etc.) such as pneumothorax, tubes and line placement, pleural effusion, lobar collapse, pneumoperitoneum, pneumonia, etc.; screening alerts (e.g., for fixed x-ray, etc.) such as tuberculosis, lung nodules, etc.; quality alerts (e.g., for mobile and/or fixed x-ray, etc.) such as patient positioning, clipped anatomy, inadequate technique, image artifacts, etc. 
     Thus, certain examples improve accuracy of an artificial intelligence algorithm. Certain examples factor in patient medical information as well as image data to more accurately predict presence of a critical finding, an urgent finding, and/or other issue. 
     Certain examples evaluate a change in a clinical condition to determine whether the condition is worsening, improving, or staying the same overtime. For example, a critical result from a chest x-ray exam is considered to be a “new or significant progression of pneumothorax”, in which the radiologist shall call the ordering practitioner and discuss the findings. Providing an AI algorithm model on an imaging device with prior imaging examines enables the model to determine whether a pneumothorax finding is new or significantly progressed and whether the finding shall be considered critical or not. 
     Although certain example methods, apparatus and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.