Patent Publication Number: US-2023143836-A1

Title: Systems and methods for monitoring medical room cleaning

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 63/263,760, filed Nov. 8, 2021, the entire contents of which are hereby incorporated by reference herein. 
    
    
     FIELD 
     This disclosure generally relates to medical room monitoring, and more specifically, to monitoring cleaning of a medical room. 
     BACKGROUND 
     Operating room cleanliness is important for preventing surgical site infection. After each surgery, cleaning personnel clean surfaces within the operating room, including surfaces of lights, tables, and control interfaces, in preparation for the next surgery. Busy surgery centers seek to reduce the amount of operating room down time to maximize the number of surgeries that can be performed in a day. Often, cleaning personnel have only ten to fifteen minutes to clean an operating room between surgeries. With this kind of time pressure, the chances of human error are not insignificant. Surfaces may be cleaned inadequately or missed altogether. Further, there is typically no way to verify whether all surfaces that should be cleaned have been cleaned, increasing the risk of surgical site infection. 
     SUMMARY 
     According to various aspects, systems and methods enable the monitoring of medical room cleaning to ensure that the surfaces of the medical room that should be cleaned between procedures have been cleaned. Imaging of a medical room may be analyzed to detect surfaces within the medical room that should be cleaned and to detect evidence that the surfaces have been cleaned. Notifications may be provided indicating which surfaces have been cleaned and/or still need cleaning. Notifications may be provided within the medical room to the cleaning personnel to assist them in keeping track of cleaning progress. Notifications may be provided to personnel outside of a medical room, for example, to enable monitoring of cleaning progress. 
     Optionally, detecting evidence that surfaces have been cleaned includes detecting signatures of cleaning on surfaces. Signatures of cleaning can include, for example, deposits left behind by a cleaning substance that are captured in imaging. Optionally, a non-visible light imaging modality may be used to detect the cleaning deposits. For example, a cleaning fluid may include a fluorescing substance that may be deposited on a surface cleaned with the cleaning fluid and a fluorescence imaging system may be used to detect the fluorescence on the cleaned surface. The presence of the fluorescing substance, or a threshold amount of fluorescing substance, on the surface may be used as an indicator that the surface has been cleaned. Signatures of cleaning can additionally or alternatively include moisture remaining on a surface after being cleaned. The moisture can be detected by a suitable imaging modality, such as short-wave infrared imaging. 
     Optionally, detecting evidence that surfaces have been cleaned includes detecting cleaning behaviors. Imaging may be analyzed to automatically identify cleaning behaviors, such as the movement of a hand over a surface by cleaning personnel. The occurrence of cleaning behaviors in proximity to a particular surface may be used as an indicator that the surface has been cleaned. Optionally, one or more machine learning models are trained to identify the cleaning procedures. For example, the machine learning model(s) may be trained to identify a hand, a cleaning implement, such as a wipe, and/or a hand grasping a cleaning implement. A determination that a given surface has been cleaned may be made, for example, when there is evidence of cleaning of the surface for a predetermined period of time and/or for a predetermined amount of the surface. 
     According to an aspect, a method for monitoring cleaning of a medical room includes receiving imaging of the medical room, the imaging capturing signatures of cleaning deposits on one or more surfaces of the medical room deposited via a cleaning process; analyzing the imaging to: identify one or more surfaces in the medical room that should be cleaned, and determine which of the one or more surfaces have been cleaned by identifying the signatures of the cleaning deposits; and displaying an indication of at least one of: (1) the surfaces that have been cleaned, and (2) one or more surfaces that have not been cleaned. 
     Optionally, the signatures of cleaning deposits are fluorescence signatures. 
     Optionally, the imaging comprises fluorescence imaging. 
     Optionally, the imaging comprises visible light imaging. 
     Optionally, the imaging was captured by at least one room mounted camera. 
     Optionally, the cleaning deposits were deposited by at least one wipe that comprises a fluorescence imaging agent. 
     Optionally, analyzing the imaging to determine which of the one or more surfaces have been cleaned comprises identifying the one or more surfaces according to a cleaning protocol. 
     Optionally, the signatures of cleaning deposits comprise moisture signatures. 
     Optionally, imaging comprises short-wave infrared (SWIR) imaging. 
     According to an aspect, a system includes one or more processors, memory, and one or more programs stored in the memory for execution by the one or more processors for: receiving imaging of the medical room, the imaging capturing signatures of cleaning deposits on one or more surfaces of the medical room deposited via a cleaning process; analyzing the imaging to: identify one or more surfaces in the medical room that should be cleaned, and determine which of the one or more surfaces have been cleaned by identifying the signatures of the cleaning deposits; and transmitting data to at least one display for displaying an indication of at least one of: (1) the surfaces that have been cleaned, and (2) one or more surfaces that have not been cleaned. 
     Optionally, the system includes at least one room mounted camera for generating the imaging. 
     Optionally, the system includes at least one room mounted fluorescence excitation light source. 
     Optionally, the system includes at least one medical light for illuminating a patient during a medical procedure comprises the at least one room mounted fluorescence excitation light source. 
     Optionally, the signatures of cleaning deposits are fluorescence signatures. 
     Optionally, the imaging comprises fluorescence imaging. 
     Optionally, the imaging comprises visible light imaging. 
     Optionally, the imaging was captured by at least one room mounted camera. 
     Optionally, the cleaning deposits were deposited by at least one wipe that comprises a fluorescence imaging agent. 
     Optionally, analyzing the imaging to determine which of the one or more surfaces have been cleaned comprises identifying the one or more surfaces according to a cleaning protocol. 
     Optionally, the signatures of cleaning deposits comprise moisture signatures. 
     Optionally, the imaging comprises short-wave infrared (SWIR) imaging. 
     According to an aspect, a method for monitoring cleaning of a medical room includes receiving imaging of the medical room; analyzing the imaging to: identify one or more surfaces in the medical room that should be cleaned, and detect performance of one or more cleaning behaviors by personnel in the medical room; determining which surfaces have been cleaned based on correlating the detected one or more cleaning behaviors with the one or more surfaces; and displaying at least one indication of at least one of: (1) the surfaces that have been cleaned, and (2) at least one of the one or more surfaces that have not been cleaned. 
     Optionally, at least one machine learning model is used to detect performance of the one or more cleaning behaviors. 
     Optionally, the at least one machine learning model is configured to detect a hand grasping a cleaning implement. 
     Optionally, the one or more surfaces in the medical room are identified using a first machine learning model and performance of the one or more cleaning behaviors is detected using a second machine learning model that is different than the first machine learning model. 
     Optionally, the performance of the one or more cleaning behaviors is detected using at least one sensor sensing contact with the one or more surfaces in addition to using the second machine learning model. 
     Optionally, determining which surfaces has been cleaned comprises determining that a cleaning procedure has been performed for a threshold amount of time. 
     Optionally, the imaging comprises imaging from multiple cameras. 
     Optionally, the at least one indication comprises a textual indication. 
     Optionally, the at least one indication is provided in an image of the medical room. 
     Optionally, the at least one indication comprises a visual indicator displayed in association with a surface in the image. 
     Optionally, the visual indicator comprises at least one of outlining of the surface and coloring of the surface. 
     Optionally, the one or more surfaces in the medical room that should be cleaned are identified at least in part based on detecting touching of the one or more surfaces by people during a medical procedure. 
     Optionally, the method includes, for a respective surface to be cleaned, providing a first visual indication in an image of the medical room that the surface should be cleaned, and replacing the first visual indication with a second visual indication upon detecting that the respective surface has been cleaned. 
     According to an aspect, a system includes one or more processors, memory, and one or more programs stored in the memory for execution by the one or more processors for: receiving imaging of the medical room; analyzing the imaging to: identify one or more surfaces in the medical room that should be cleaned, and detect performance of one or more cleaning procedures by personnel in the medical room; determining which surfaces have been cleaned based on correlating the detected one or more cleaning procedures with the one or more surfaces; and transmitting data to at least one display for displaying at least one indication of at least one of: (1) the surfaces that have been cleaned, and (2) at least one of the one or more surfaces that have not been cleaned. 
     Optionally, the system is configured to use at least one machine learning model to detect performance of the one or more cleaning behaviors. 
     Optionally, the at least one machine learning model is configured to detect a hand grasping a cleaning implement. 
     Optionally, the one or more surfaces in the medical room are identified using a first machine learning model and performance of the one or more cleaning behaviors is detected using a second machine learning model that is different than the first machine learning model. 
     Optionally, the performance of the one or more cleaning behaviors is detected using at least one sensor sensing contact with the one or more surfaces in addition to using the second machine learning model. 
     Optionally, determining which surfaces has been cleaned comprises determining that a cleaning procedure has been performed for a threshold amount of time. 
     Optionally, the imaging comprises imaging from multiple cameras. 
     Optionally, the at least one indication comprises a textual indication. 
     Optionally, the at least one indication is provided in an image of the medical room. 
     Optionally, the at least one indication comprises a visual indicator displayed in association with a surface in the image. 
     Optionally, the visual indicator comprises at least one of outlining of the surface and coloring of the surface. 
     Optionally, the one or more surfaces in the medical room that should be cleaned are identified at least in part based on detecting touching of the one or more surfaces by people during a medical procedure. 
     Optionally, the system is configured for, for a respective surface to be cleaned, providing a first visual indication in an image of the medical room that the surface should be cleaned, and replacing the first visual indication with a second visual indication upon detecting that the respective surface has been cleaned. 
     It will be appreciated that any of the variations, aspects, features and options described in view of the systems apply equally to the methods and vice versa. It will also be clear that any one or more of the above variations, aspects, features and options can be combined. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The invention will now be described, by way of example only, with reference to the accompanying drawings, in which: 
         FIG.  1    is a schematic illustration of an exemplary operating room that includes an example of a monitoring system for monitoring cleaning of the room; 
         FIG.  2    is a flow diagram of an exemplary method for monitoring the cleaning of a medical room; 
         FIG.  3    illustrates an example of the identification of one or more surfaces in an exemplary image of a medical room; 
         FIG.  4    illustrates an example of the detection of a cleaning deposit in an exemplary image of a medical room; 
         FIG.  5 A  illustrates an example of a first imaging mode image used for detecting surfaces, and  FIG.  5 B  illustrates an example of a second imaging mode image used for detecting signatures of cleaning; 
         FIG.  6    illustrates an exemplary method for determining surfaces that have been cleaned by identifying cleaning behaviors in imaging; 
         FIG.  7    illustrates an example of determining whether a hand and a cleaning implement are detected at the same location as one another and as a surface to be cleaned detected; 
         FIG.  8    illustrates an exemplary user interface for providing notifications of which surfaces have been cleaned; 
         FIGS.  9 A- 9 C  illustrate exemplary methods for training machine learning models to perform surface detection, cleaning behavior detection, and cleaning deposit detection, respectively; 
         FIG.  10    illustrates an example of a computing system; and 
         FIG.  11    illustrates an exemplary user interface that a user may use to select a cleaning tracking profile that a cleaning tracking system may use for tracking cleaning in a particular medical room. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to implementations and examples of various aspects and variations of systems and methods described herein. Although several exemplary variations of the systems and methods are described herein, other variations of the systems and methods may include aspects of the systems and methods described herein combined in any suitable manner having combinations of all or some of the aspects described. 
     Systems and methods, according to the principles described herein, can provide automatic monitoring of medical room cleaning, helping to ensure that medical rooms are properly cleaned between procedures. This can reduce the risk of patient infection and improve treatment outcomes. According to various aspects, imaging of a medical room is captured by one or more in-room cameras before, during, and/or after a medical procedure. The imaging is analyzed to automatically determine whether surfaces that should be cleaned have been cleaned. The cleaning crew within the medical room could be notified of surfaces that have and/or have not been cleaned to help them track their progress. Additionally or alternatively, information about which surfaces have been cleaned can be used for supervision of the cleaning crew and/or recording cleaning history. 
     According to an aspect, imaging of a medical room is analyzed to identify signatures of cleaning on surfaces that should be cleaned between medical procedures in the medical room. Cleaning protocols may designate certain surfaces in the medical room for cleaning between procedures. For example, surfaces of the top portion of a surgical table, surgical carts, surgical lights, and surgical equipment used in or near the sterile field may each be designated for cleaning between procedures. These surfaces may be monitored for identifying signatures of cleaning. This can include automatically identifying moisture on surfaces left behind by a cleaning fluid, automatically identifying chemical deposits associated with a component of a cleaning fluid, and/or automatically identifying indications of cleaning behavior by the cleaning crew. A determination that a designated surface has been cleaned may be made when, for example, a signature of cleaning is identified that corresponds with the designated surface. 
     According to various aspects, one or more machine learning models are used for analyzing imaging to identify surfaces designated for cleaning. The one or more machine learning models may be trained to identify surfaces in a medical room that should be cleaned. For example, surfaces that should be cleaned may be labeled in training images and the labeled training images may be used for training a machine learning model to automatically identify the similar surfaces in imaging. The surfaces identified in the imaging may be analyzed for identification of cleaning signatures. In some variations, once a surface that is designated to be cleaned has been identified, the surface is monitored for signatures of cleaning. In some variations, designated surfaces and signatures of cleaning are identified in parallel and a correlation between them is used to determine whether the surfaces to be cleaned have been cleaned. 
     According to an aspect, identifying signatures of cleaning includes identifying cleaning behavior in the imaging. Cleaning behavior may be, for example, the movement of a hand and cleaning implement (e.g., a cleaning cloth) in proximity to a surface to be cleaned. One or more machine learning models may be trained to identify hands, cleaning implements, and/or other features associated with cleaning that may appear in imaging, and the presence of, for example, hands with cleaning implements in proximity to a surface to be cleaned for a sufficient period of time may trigger a determination that a given surface has been cleaned. 
     According to an aspect, identifying signatures of cleaning includes identifying deposits associated with cleaning on surfaces to be cleaned. In some variations, this can include identifying moisture left behind by cleaning, for example, using short-wave infrared (SWIR) imaging. An imaging modality such as SWIR could be used to detect multiple different signatures of cleaning, including multiple different types of cleaning fluids. In some variations, a cleaning substance may include a dye that deposits on surfaces during cleaning and remains on surfaces for a sufficient period of time to allow for detection of the deposits after cleaning but dissipates sufficiently quickly that the dye is no longer detectable on the surfaces after a subsequent medical procedure. The dye could be invisible to the naked eye and may be detectable using a suitable imaging modality. For example, the dye could be a fluorescing dye that fluoresces in a non-visible wavelength and/or fluoresces in response to a non-visible fluorescence excitation light. Visible light imaging could be used to identify the surfaces to be cleaned, as discussed above, and a dye-detecting imaging modality (such as an infrared imaging modality) may be used to detect the dye. The imaging modalities may be analyzed to determine whether the dye is detected in portions of the imaging associated with surfaces to be cleaned. 
     Systems and methods according to the principles described herein can facilitate the proper cleaning of medical rooms by providing information to cleaning crews and/or supervisory personnel about cleaning progress. By helping to ensure proper cleaning of medical rooms, the systems and methods can help improve patient safety by reducing the risk of infection associated with pathogens in the medical room that can be eliminated through proper cleaning. 
     In the following description, it is to be understood that the singular forms “a,” “an,” and “the” used in the following description are intended to include the plural forms as well, unless the context clearly indicates otherwise. It is also to be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It is further to be understood that the terms “includes, “including,” “comprises,” and/or “comprising,” when used herein, specify the presence of stated features, integers, steps, operations, elements, components, and/or units but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, units, and/or groups thereof. 
     Certain aspects of the present disclosure include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present disclosure could be embodied in software, firmware, or hardware and, when embodied in software, could be downloaded to reside on and be operated from different platforms used by a variety of operating systems. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that, throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” “generating” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission, or display devices. 
     The present disclosure in some examples also relates to a device for performing the operations herein. This device may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, computer readable storage medium, such as, but not limited to, any type of disk, including floppy disks, USB flash drives, external hard drives, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. Suitable processors include central processing units (CPUs), graphical processing units (GPUs), field programmable gate arrays (FPGAs), and ASICs. 
     The methods, devices, and systems described herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein. 
       FIG.  1    is a schematic illustration of an exemplary operating room  10  that includes a monitoring system  100  for monitoring cleaning of the room. Monitoring system  100  includes a camera system  102  for imaging the operating room and a computing system  104  configured for analyzing imaging data generated by the camera system  102  for automatically detecting cleaning of designated surfaces of equipment and devices in the operating room. Examples of equipment and devices typically found in an operating room that may require cleaning between procedures include an operating table  112 , one or more carts  110 , one or more surgical lights  114 , and one or more displays  108 . 
     The camera system  102  generates imaging of the operating room. The imaging can include single images or video frames. The camera system  102  can include one or more cameras of any suitable type. For example, the camera system  102  may include one or more pan, tilt, zoom (PTZ) cameras. The one or more cameras of the camera system  102  may be positioned to capture a sufficient amount of the operating room for monitoring cleaning of the equipment and devices in the operating room. The camera system  102  may continuously image the operating room or may periodically image the operating room. The camera system  102  may include any suitable imaging modality or combination of imaging modalities. The camera system  102  may include visible light imaging, infrared imaging, ultraviolet imaging, or any combination of these. In some variations, the camera system  102  includes one or more illuminators  116  for illuminating the scene. This may be useful, in particular, for non-visible illumination of the scene in support of non-visible light imaging modalities. One or more illuminators  116  could be included with one or more cameras of the camera system  102 , as illustrated, or could be provided in a separate location. For example, an illuminator  118  could be incorporated into a surgical light  114  or other lighting within the medical room  10 , including ceiling lights. 
     Computing system  104  receives imaging from the camera system  102  and automatically detects cleaning of the equipment and/or devices in the operating room that should be cleaned. The computing system  104  may be communicatively connected to one or more displays  108  in the operating room for displaying information related to the cleaning of the operating room. The computing system  104  may additionally or alternatively be connected to a remote system  106  for communicating information related to the cleaning of the operating room to the remote system  106 . The remote system  106  may include a display, such as located at a nurses station, for displaying information related to the cleaning to personnel outside of the operating room for monitoring the cleaning of the operating room. The remote system  106  may be or include a record keeping system for keeping records of the cleaning of medical rooms. 
       FIG.  2    is a flow diagram of an exemplary method  200  for monitoring the cleaning of a medical room, such as operating room  10  of  FIG.  1   . Method  200  could be performed, for example, by computing system  104  of  FIG.  1   . Method  200  could be performed continuously or periodically, such as in the period between medical procedures in a medical room. Method  200  could be performed in response to a user request. For example, personnel could initiate method  200  upon completion of a medical procedure or cleaning personnel, or medical room monitoring personnel may initiate method  200  at the start of a cleaning procedure. 
     At step  202 , imaging of a medical room are received by the computing system. The imaging may be one or more images and/or one or more video frames or a series of images and/or video frames. The imaging can include imaging captured by multiple different cameras. The imaging captures at least a portion of a medical room, including one or more objects in the medical room that should be cleaned. For example, with reference to  FIG.  1   , the imaging may capture at least a portion of operating table  112 , cart  110 , surgical light  114 , and display  108 . The imaging may be generated by one or more camera systems, such as one or more PTZ cameras or any other suitable types of cameras. The imaging may include visible light imaging, non-visible light imaging, or any combination of visible and non-visible light imaging. 
     At step  204 , at least some of the imaging received at step  202  is analyzed to identify in the imaging one or more surfaces that should be cleaned. This may be done using a machine learning model that is trained to identify designated surfaces and/or objects in the medical room. For example, with reference to  FIG.  1   , a machine learning model, such as classifier, may be trained to identify in the imaging one or more of table  112 , cart  110 , surgical light  114 , and display  108  and/or one or more surfaces of these objects. 
       FIG.  3    illustrates an example of the result of the identification of one or more surfaces in imaging.  FIG.  3    includes an image  300  of a medical room that captures the table  112 , cart  110 , surgical light  114 , and a device  350  located on the cart  110 . A top surface  302  of the table  112 , a top surface  304  of the cart  110 , the outer surface  316  of the surgical light  114 , and a boom arm  308  supporting the surgical light  114  have been identified in the image, according to step  204  of method  200 . The identified surfaces may be types of surfaces that are designated for cleaning according to a predetermined cleaning protocol. For example, a cleaning protocol for an operating room may designate the top surface of a surgical table, top surfaces of carts, the outer surface of the surgical light, boom arms, and devices positioned are carts for cleaning between procedures, and a machine learning model may be trained to identify these types of surfaces and any other types of surfaces designated for cleaning. The machine learning model may be trained such that it does not identify surfaces or objects that are not supposed to be cleaned according to the cleaning protocol. The machine learning model may be configured to generate a bounding box for each identified object and/or surface. The example illustrated in  FIG.  3    includes bounding box  310  for surface  302 , bounding box  312  for surface  304 , bounding box  314  for device  350 , bounding box  316  for surgical light  114 , and bounding box  318  for boom arm  308 . 
     The machine learning model may include a single classifier that classifies regions of the image as either a designated surface or not. Alternatively, the machine learning model may include multiple classifiers that are capable of identifying a type of a given surface. For example, the machine learning model may not only identify that surface  302  and  304  are surfaces that should be cleaned but also that surface  302  is a surface of a surgical table and surface  304  is a surface of a cart. 
     Returning to  FIG.  2   , method  200  continues with step  206  in which the computing system determines whether one or more of the surfaces identified in step  204  has been cleaned. The imaging is analyzed to automatically detect signatures of cleaning associated with the identified surfaces. According to some variations, the signatures of cleaning associated with the identified surfaces are deposits on the surfaces that are detectable in the imaging. Deposits may be, for example, a component of a cleaning fluid used for cleaning the surface. The cleaning fluid could be, for example, a spray-on cleaning fluid or a cleaning fluid absorbed into cleaning wipes. The component of the cleaning fluid may remain on a surface for a sufficient length of time after the surface has been cleaned to enable detection of the component in the imaging but sufficiently volatile that the component breaks down or dissipates from the surface within a suitable period of time so that the surface does not appear to be clean after it has been used again. The component could be, for example, a dye that is visible in the imaging. The dye may be invisible to the naked eye but detectable with a non-visible light imaging modality. For example, the dye may be a fluorescence imaging agent that fluoresces in a non-visible wavelength. Alternatively, the fluorescence imaging agent could fluoresce in a visible wavelength but only in response to a non-visible excitation wavelength such that the fluorescence imaging agent is invisible to the naked eye until illuminated with the non-visible excitation wavelength. The fluorescence imaging agent could be sufficiently volatile that the fluorescence imaging agent loses its fluorescing properties over a sufficiently short period of time that the fluorescence imaging agent is no longer detectable in fluorescence imaging during a medical procedure following the cleaning procedure. For example, the fluorescence imaging agent may be formulated to lose its fluorescing properties within 30 minutes after being deposited on a cleaned surface, preferably within 20 minutes after being deposited on a cleaned surface, more preferably within 15 minutes after being deposited on a cleaned surface, or most preferably within 10 minutes after being deposited on a cleaned surface. The fluorescence imaging agent may be formulated to maintain its fluorescing properties after being deposited on a cleaned surface for at least 1 minute, for at least 2 minutes, for at least 5 minutes, or for at least 10 minutes. 
     With reference to  FIG.  1   , an imaging system, such as imaging system  102  of system  100 , may include illuminator  116  and/or illuminator  118  for providing illumination for detecting the dye or other cleaning deposit. The illuminators  116 ,  118  could be, for example, infrared (e.g., near infrared, shortwave infrared, etc.) or ultraviolet illumination sources. 
     Another example of deposits indicative of cleaning that may be detected is moisture left behind after a surface has been cleaned. A suitable imaging modality may be used to detect the moisture on the surface. For example, short wave infrared (SWIR) imaging may be used to detect the moisture left behind after cleaning since moisture has a high absorption rate at SWIR wavelengths. SWIR could be used to detect one or more cleaning fluids (not just the moisture in them) based on the spectral signature. SWIR could be used to differentiate between different cleaning fluids to the extent the different cleaning fluids have different spectral signatures. 
       FIG.  4    illustrates the detection of a cleaning deposit in an exemplary image of a medical room, according to aspects of the principles discussed above. The image  400  captures table  112 . A top surface  402  of the table  112  has been detected in the image  400 . Cleaning deposits  404  have been detected in the portion of the image corresponding to the identified top surface  402  of the table  112 . The cleaning deposits  404  can include one or more cleaning fluids not wiped from the surface or one or more constituents of one or more cleaning fluids, such as moisture or a dye. A determination that the surface  402  has been cleaned may be based on the presence of the cleaning deposits  404  in the portion of the image  400  corresponding to the surface  402 . The determination that the surface has been cleaned may be based on a threshold amount of cleaning deposits  404  being detected and/or a threshold coverage of the surface  402 . 
     The identification of surfaces to be cleaned and the detection of signatures of cleaning may use different imaging modalities. For example, a visible light imaging modality may be used for detection of surfaces and a non-visible light imaging modality may be used for detecting cleaning deposits. The information from these two imaging modalities may be combined to determine whether a given surface has been cleaned.  FIG.  5 A  illustrates an exemplary first image  500  generated using a first imaging modality and  FIG.  5 B  illustrates an exemplary second image  550  generated using a second imaging modality. The first image  500  is used for detecting a surface  502  that should be cleaned, as indicated by bounding box  504 , according to the principles discussed above. The first image  500  could be, for example, a visible light image. The second image  550  is used for detecting deposits  552  that are captured using the second imaging mode but are not discernable using the first imaging mode. A computing system may identify the signatures of the deposits  552  in the second image  550 , such as based on pixel values that are above or below a predetermined intensity threshold or by using any other suitable image processing technique. In some variations, a machine learning model is trained to detect signatures of deposits  552 . The machine learning model used to detect signatures of deposits may be different than the machine learning model used to detect the surfaces in the imaging. 
     The first image  500  and second image  550  may be registered to one another and the computing system may determine whether the cleaning deposits  552  are located in the region of the second image  550  that corresponds to the surface  502  identified in the first image  500 . Any suitable thresholds may be used to determine whether sufficient signatures of cleaning deposits have been detected to designate a given surface as having been cleaned. For example, pixel intensities could be compared with a threshold such that intensities that are too low are not counted as indicative of cleaning, and/or a threshold for a number or percentage of pixels or other measure of surface coverage corresponding with the surface to be cleaned could be used. 
     According to some variations, signatures of cleaning associated with the surfaces that are identified in imaging according to step  206  include one or more cleaning behaviors. Imaging may be analyzed while cleaning personnel are cleaning a medical room and various actions of the cleaning personnel captured in imaging may be analyzed to determine whether the actions indicate cleaning of one or more surfaces to be cleaned. For example, imaging may be analyzed to detect a hand or a hand holding a cleaning implement that is in proximity to (e.g., on top of) a surface to be cleaned. Additionally or alternatively, motion of a hand or a hand holding a cleaning implement, motion of the cleaning implement by itself, or any other suitable movement-based indication of a cleaning operation may be detected in the imaging. 
     Cleaning behavior may be detected using, for example, a machine learning model trained to identify the cleaning behavior or aspects of the cleaning behavior in the imaging. For example, a machine learning model may be trained to identify a hand, a hand holding a cleaning implement, and/or the cleaning implement by itself in imaging. This machine learning model may be different than the machine learning model used to detect the surfaces in the image or a single machine learning model may be configured to detect surfaces and to detect the indicators of cleaning behavior. When cleaning behaviors are detected for an identified surface to be cleaned, the surface may be designated as having been cleaned. 
       FIG.  6    illustrates an exemplary method  600  for determining surfaces that have been cleaned by identifying cleaning behaviors in imaging. Method  600  may be used, for example, for steps  204  and  206  of method  200  of  FIG.  2    and may be performed, for example, by a computing system such as computing system  104  of  FIG.  1   . At step  602 , surfaces and/or objects are detected and/or tracked in imaging (e.g., a video frame) received from an imaging system, such as camera system  102  of  FIG.  1   , as discussed above with respect to step  204  of method  200 . Once a given surface or object has been detected for the first time, the surface or object may thereafter be tracked by the computing system using any suitable object tracking method. This may reduce the computational load since object tracking can be less computationally intensive than object detection. 
     At step  604  and  606 , the computing system analyzes the imaging to detect objects associated with cleaning, which in this example are a hand and a cleaning implement, respectively. The computing system may be configured to detect any number of hands and/or cleaning implements. For example, the computing system may detect a hand and cleaning implement of a first cleaning personnel cleaning a first surface and simultaneously detect a hand and cleaning implement of a second cleaning personal cleaning a second surface. The number of hands, cleaning implements, and/or other objects associated with cleaning behaviors that may be detected is not limited. A machine learning model, such as a classifier, may be used to detect the hand and cleaning implement. The machine learning model may analyze the entire image or may analyze one or more regions of interest in the image. For example, the machine learning model may analyze only those portions of the image associated with a surface and/or object detected or tracked in step  602 . The depiction of steps  604  and  606  as separate steps is merely illustrative. The detection of one or more hands and one or more cleaning implements can be done simultaneously by the same machine learning model. Generally, a machine learning model can be trained to detect multiple types of objects that are indicative of cleaning behavior simultaneously. 
     At step  608 , a determination is made whether a hand and a cleaning implement are detected at the same location as one another and as a surface to be cleaned that was detected at step  602 . Any suitable test can be used for determining whether a hand and cleaning implement are at the same location as one another and as a surfaces to be cleaned. For example, the hand and cleaning implement may be determined to be at the same location if, for example, bounding boxes for the hand and cleaning implement overlap in any amount or by a threshold amount or if a bounding box for one is completely enclosed by a bounding box for the other. The same conditions or any other suitable conditions could be used for determining whether the hand and/or cleaning implement are located at the same location as a surface to be cleaned. 
       FIG.  7    illustrates an example of determining whether a hand and a cleaning implement are detected at the same location as one another and as a detected surface to be cleaned, according to step  608 . In the image  700  of  FIG.  7   , a surface  702  of surgical table  704  has been detected and a bounding box  706  that encompasses the surface  702  has been generated, according to step  602 . A hand  708  has been detected and a bounding box  710  that encompasses the hand  708  has been generated, according to step  604 . Similarly, a cleaning implement  712  (depicted as a cleaning cloth) has been detected and a bounding box  714  that encompasses the cleaning implement  712  has been generated, according to step  606 . Because the bounding box  714  of the cleaning implement  712  and the bounding box  710  of the hand  708  overlap with one another and overlap the bounding box  706  of the surface  702 , the determination may be made, according to step  608 , that the hand  708 , cleaning implement  712 , and surface  702  are at the same location. 
     If a hand and cleaning implement are determined at step  608  to be at the same location as one another and as the surface, then a counter may be started or incremented at step  610 . The counter may be used to ensure that the hand and cleaning implement are at the same location as one another and as the surface over a relatively extended period of time that is indicative of cleaning, instead of, for example, a mere passage of the hand and cleaning implement over the surface. The counter can be time based (e.g., a certain number of seconds or minutes) or can be instance based (e.g., a certain number of consecutive video frames or images). Where a hand and cleaning implement are detected in the same location as one another and/or as a surface for the first time, the counter can be initiated, and where the hand and cleaning implement are detected in the same location as one another and as the surface once the counter has already been initiated, the counter may be incremented (or allowed to continue to run, where the counter is a timer). A separate counter may be initiated and/or incremented for each distinct surface and/or portion of a surface in the imaging. 
     At step  612 , the counter value is compared to a threshold to determine whether the hand and cleaning implement have been detected in the same location as one another and as the surface for a sufficient amount of time. If so, then the determination may be made at step  614  that the surface has been cleaned. If not, then method  600  may return to steps  604  and  606  for any further imaging received by the computing system. 
     If a hand, cleaning implement, and surface are determined not to be at the same location at step  608 , then method  600  returns to steps  604  and  606  for analyzing any further imaging received by the computing system. If a counter had previously been initiated and/or incremented, the counter may be cleared at step  616 . 
     The steps of method  600  may be performed for any number of detected surfaces and/or objects. The presence of hands and cleaning implements at surface A may be tracked at the same time as the presence of hands and cleaning implements at surface B, with different counters being used. Additional conditions for determining whether a surface has been cleaned may be used. For example, a detected surface may be subdivided into regions and method  600  may be performed for each region to ensure that the entire surface is cleaned, not just a portion of the surface. The surface may not be designated as being clean until each subdivided region is determined as having been cleaned. 
     Method  600  may be performed continuously throughout a room cleaning process. Upon determining that a given surface has been cleaned, the region of interest associated with the surface may no longer be analyzed to detect cleaning, such as to conserve computing power. Alternatively, the cleaning of a given surface may continue to be tracked even after the surface has been determined to be cleaned. This could be useful for tracking aspects of cleaning other than whether a surface has been cleaned, such as for determining whether cleaning personnel spent too long on a given surface or cleaned a given surface more than once. 
     Method  600  is merely an example of determining whether a surface has been cleaned. Other methods can include, for example, detecting a cleaning motion, such as by detecting frame-by-frame movements of a detected hand and/or cleaning implement over a surface. In some variations, a machine learning model is configured to detect a hand grasping a cleaning implement, rather than detecting a hand and separately detecting a cleaning implement. 
     In some variations, non-imaging information may be used in conjunction within imaging information to determine whether a surface has been cleaned. For example, one or more sensors may be used to detect contact of surfaces to be cleaned associated with cleaning. For example, with reference to  FIG.  1   , a capacitive sensor system  120  may be used to detect touching of the surgical table by cleaning personnel. Any suitable sensor system can be used to detect touching, including, for example, one including one or more pressure sensors. The detection of touching of a given surface to be cleaned can be used as a check on the image-based detection of cleaning such that, for example, a surface is not determined as being cleaned unless touching of the surface has also been detected. 
     Returning to  FIG.  2   , method  200  continues with step  208  in which one or more notifications are provided indicating which surfaces and/or objects have been cleaned and/or which surfaces and/or objects have not been cleaned. One more notifications can be provided within the medical room, such as for notifying cleaning personnel of their progress. One or more notifications could be provided outside of the medical room, including for example, to a nurses station or other location for personnel to supervise and/or track room cleaning. Notifications can be provided on a continuous basis as the cleaning crew makes progress. For example, upon a determination being made that a given surface has been cleaned, a notification can be provided that the surface has been cleaned. Notifications can be provided in any suitable manner. In some variations, a user interface is displayed that shows which surfaces designated for cleaning have been cleaned and/or have not yet been cleaned. The user interface could include a list of surfaces designated for cleaning which a graphical indication of the cleaning status of each surface. A user interface could include an image of the room with graphical indicators provided for each displayed surface indicated whether the surface has been cleaned. 
       FIG.  8    illustrates an exemplary user interface  800  for providing notifications of which surfaces have been cleaned. User interface  800  may be displayed on a display  850  located within the medical room or located externally of the medical room. User interface  800  includes an image  802  of at least a portion of an operating room, including one or more surfaces of the operating room that are supposed to be cleaned according to a predetermined cleaning protocol. In the illustrated example, the image  802  includes an operating table  804 , surgical light  806 , and cart  808  of the operating room. Graphical indicators are provided for each of the surfaces of the operating room designated for cleaning, including indicator  810  for the top surface of the operating table  804 , indicator  812  for the top surface of the cart  808 , and indicator  814  for the surgical light  806 . The indicators may be differently colored or otherwise visually varied to indicate the cleaning status of the respective surface. For example, the different appearance of indicator  810  for the top surface of the operating table  804  relative to indicator  812  and  814  may indicate that the top surface of the operating table  804  has been cleaned but the top surface of the cart  808  and the surgical light  806  have not yet been cleaned. For example, green boxes and/or shading can be used to indicate a cleaned surface and red boxes and/or shading can be used to indicate an uncleaned surface. The indicator used for a given surface may change upon a change in the cleaning status of a surface. For example, a surface provided with a red indicator indicating that the surface has not yet been cleaned and may change to green, indicating that the surface is clean, upon the system determining that the surface has been cleaned. 
     Additionally or alternatively, the graphical user interface  800  can include a checklist  818  or other text-based means for indicating which surfaces have been cleaned. In the illustrated example, the checklist  818  includes a list of the surfaces to be cleaned, with a strikethrough font used to indicate that the operating table  804  has been cleaned. The line items in the checklist  818  can be populated based on the surfaces detected in the medical room 
     The graphical user interface  800  may be updated upon the computing system determining that a surface has been cleaned. For example, upon completion of cleaning of the cart  808 , the indicator  812  may change colors from red to green and/or the checklist entry for the cart may change font. 
     In addition to or instead of providing a notification of surfaces that have been cleaned, cleaning progress may be stored in one or more databases. Storing such records could enable periodic auditing of the cleaning of medical rooms in a given facility. Such information could be included or otherwise linked to a medical record of a patient whose procedure follows the medical room cleaning. Cleaning history and patient treatment outcome could be analyzed to determine whether room cleaning status correlates to treatment outcomes. 
     As explained above, one or more machine learning models may be used to detect objects and/or surfaces in a medical room and to detect whether surfaces have been cleaned.  FIGS.  9 A- 9 C  illustrate exemplary methods for training machine learning models to perform these tasks.  FIG.  9 A  is an exemplary block diagram of a method  900  for training a machine learning model to detect objects and/or surfaces to be cleaned, which could be used, for example, for step  204  of method  200 . At step  902 , images of objects and/or surfaces are collected. The images include images of medical rooms that include objects and/or surfaces of the type that are desired to be tracked for cleaning. At step  904 , the objects and/or surfaces in the images are labeled, such as by using manually defined bounding boxes. The surfaces and/or objects labeled in the images may be the types of surfaces and/or objects that are designated for cleaning according to a predetermined cleaning protocol. At step  906 , a machine learning model is trained on the labeled images to identify the objects and/or surfaces in the images. Different models may be trained for different applications. For example, the training data set for a model used for tracking cleaning in a surgical room may be different than the training data set for a model used for tracking cleaning in a non-surgical room, which may require less thorough cleaning than the surgical room. 
       FIG.  9 B  is an exemplary block diagram of a method  920  for training a machine learning model to detect cleaning behavior, which could be used for step  206  of method  200  and/or steps  604  and  606  of method  600 . At step  922 , images and/or video that depict cleaning similar to the types of cleaning performed in medical rooms are collected. At step  924 , indicators associated with cleaning are labeled in the imaging. Any suitable indicators of cleaning behaviors in the imaging may be labeled. This can include hands of cleaning personnel, cleaning implements, such as wipes, the surfaces that are being cleaned, and/or labeling associated with the time period in which a surface is being cleaned. At step  926 , the machine learning model is trained on the labeled images. Depending on how the machine learning model is trained, the output of the machine learning model could be indications associated with detections of each indicator for one or more regions of interest in imaging, such as described above with respect to steps  604  and  606  of method  600 . Alternatively, a machine learning model may be trained to output that a cleaning operation has been detected for a given region of interest (for example, incorporating steps  604 ,  606 , and  608  of method  600  into the functionality of the machine learning model). In some variations, the machine learning model is trained to incorporate a temporal element into detection of cleaning behavior. For example, the machine learning model may be trained to determine that a surface has been cleaned only after a cleaning operation is detected for a given surface for a suitable period of time. 
       FIG.  9 C  is an exemplary block diagram of a method  940  for training a machine learning model to detect deposits on surfaces that indicate that the surfaces have been cleaned, which could be used for step  206  of method  200 . At step  942 , imaging (images and/or video) of surfaces is collected. The imaging was generated using an imaging modality that is capable of detecting the desired cleaning deposits. For example, where the cleaning deposits include a fluorescence imaging agent that fluoresces in the infrared spectrum, fluorescence imaging generated by an infrared fluorescence imaging modality is used, or where the cleaning deposits are detectable using a SWIR imaging modality, SWIR imaging is used. At step  944 , surfaces in the imaging are labeled according to the presence or absence of cleaning deposits. Any desired number of cleaning deposits that are captured in the imaging may be labeled. For example, where different cleaning agents may be used that have different spectral signatures, the imaging may be labeled according to the specific cleaning agent—e.g., cleaning agent A, cleaning agent B, cleaning agent C in  FIG.  9 C . Surfaces without cleaning agent may be labeled as well. At step  946 , the machine learning model is trained to identify the spectral signatures of the respective cleaning agents. The machine learning model may be trained to detect the presence of a cleaning agent, according to the types of cleaning agents labeled in the training images, and may optionally be trained to detect the type of cleaning agent as well. 
     As noted above, different machine learning models may be trained for different applications. For example, the training data set for a model used for tracking cleaning in a surgical room may be different than the training data set for a model used for tracking cleaning in a non-surgical room and/or the training data set for a model used for tracking cleaning after a particular type of medical procedure may be different than the training set for a model used for tracking cleaning after a different type of medical procedure. Alternatively, a machine learning model may be trained for tracking cleaning in multiple types of medical rooms and/or for multiple types of procedures and cleaning tracking system may be configurable by a user for a particular type of medical room and/or medical procedure. 
     A user may set up different cleaning tracking profiles for different medical rooms and may customize each profile by selecting different surfaces to track and/or by selecting different cleaning behaviors to track. For example, computing system  104  and/or remote system  106  may include a system configuration user interface that a user may use to tailor the system for a particular type of medical room and/or a particular type of medical procedure.  FIG.  11    illustrates an exemplary user interface  1100  that a user may use to select a cleaning tracking profile that the cleaning tracking system may use for tracking cleaning in a particular medical room (“Medical Room #1). User interface  1100  includes a room type menu  1102  for selecting predefined room type profiles that may define which surfaces are tracked and/or which cleaning behaviors are tracked for the particular medical room. In the illustrated example, “operating room type 2” has been selected. A procedure type menu  1104  may be provided for predefined cleaning protocols associated with different procedure types. A surfaces menu  1106  can enable customization of the surfaces that the system will track for the particular medical room. In the illustrated example, “operating table,” “cart,” “surgical light,” and “medical device type 2” are selected per the “operating room type 2” profile. A user may be able to select and/or deselect surfaces for further customization. User interface  1100  also includes a cleaning indicators menu  1108  that can be used for selecting different cleaning indicators that the system will track. The illustrated example includes “wiping,” in which the system will track cleaning behaviors as evidence of cleaning, and “moisture,” “cleaning agent 1,” and “cleaning agent 2,” in which the system will track moisture and/or the two different cleaning agents as evidence of cleaning. In the illustrated example, “wiping” is preselected as part of the operating room type 2 profile and this can be further customized by selecting or deselecting the available cleaning indicators. A user interface such as user interface can be used to set predetermined cleaning tracking profiles for each medical room of a facility. 
       FIG.  10    illustrates an example of a computing system  1000  that can be used for one or more of components of system  100  of  FIG.  1   , such as one or more of camera system  102 , computing system  104 , and remote system  106 . System  1000  can be a computer connected to a network, such as one or more networks of hospital, including a local area network within a room of a medical facility and a network linking different portions of the medical facility. System  1000  can be a client or a server. As shown in  FIG.  10   , system  1000  can be any suitable type of processor-based system, such as a personal computer, workstation, server, handheld computing device (portable electronic device) such as a phone or tablet, or dedicated device. The system  1000  can include, for example, one or more of input device  1020 , output device  1030 , one or more processors  1010 , storage  1040 , and communication device  1060 . Input device  1020  and output device  1030  can generally correspond to those described above and can either be connectable or integrated with the computer. 
     Input device  1020  can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, gesture recognition component of a virtual/augmented reality system, or voice-recognition device. Output device  1030  can be or include any suitable device that provides output, such as a display, touch screen, haptics device, virtual/augmented reality display, or speaker. 
     Storage  1040  can be any suitable device that provides storage, such as an electrical, magnetic, or optical memory including a RAM, cache, hard drive, removable storage disk, or other non-transitory computer readable medium. Communication device  1060  can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computing system  1000  can be connected in any suitable manner, such as via a physical bus or wirelessly. 
     Processor(s)  1010  can be any suitable processor or combination of processors, including any of, or any combination of, a central processing unit (CPU), graphics processing unit (GPU), field programmable gate array (FPGA), and application-specific integrated circuit (ASIC). Software  1050 , which can be stored in storage  1040  and executed by one or more processors  1010 , can include, for example, the programming that embodies the functionality or portions of the functionality of the present disclosure (e.g., as embodied in the devices as described above). For example, software  1050  can include one or more programs for execution by one or more processor(s)  1010  for performing one or more of the steps of method  200  and/or method  600 . 
     Software  1050  can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage  1040 , that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device. 
     Software  1050  can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport computer readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation medium. 
     System  1000  may be connected to a network, which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines. 
     System  1000  can implement any operating system suitable for operating on the network. Software  1050  can be written in any suitable programming language, such as C, C++, Java, or Python. In various examples, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. 
     The foregoing description, for the purpose of explanation, has been described with reference to specific examples. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The examples were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various examples with various modifications as are suited to the particular use contemplated. 
     Although the disclosure and examples have been fully described with reference to the accompanying figures, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims. Finally, the entire disclosure of the patents and publications referred to in this application are hereby incorporated herein by reference.