TREATMENT OUTCOME PREDICTION FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION USING BASELINE CHARACTERISTICS

A method and system for predicting a treatment outcome. Three-dimensional imaging data for a retina of a subject is received. A first output is generated using a deep learning system and the three-dimensional imaging data. The first output and baseline data are received as input for a symbolic model. A treatment outcome is predicted, via the symbolic model, for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

FIELD

This description is generally directed towards predicting treatment outcomes in subjects diagnosed with age-related macular degeneration. More specifically, this description provides methods and systems for predicting treatment outcomes in subjects diagnosed with neovascular age-related macular degeneration (nAMD) using baseline data identified for the subjects.

BACKGROUND

Age-related macular degeneration (AMD) is a disease that impacts the central area of the retina in the eye, which is referred to as the macula. AMD is a leading cause of vision loss in subjects 50 years or older. Neovascular AMD (nAMD) is one of the two advanced stages of AMD. With nAMD, new and abnormal blood vessels grow uncontrollably under the macula. This type of growth may cause swelling, bleeding, fibrosis, other issues, or a combination thereof. The treatment of nAMD typically involves an anti-vascular endothelial growth factor (anti-VEGF) therapy (e.g., an anti-VEGF drug such as ranibizumab). The retina's response to such treatment is at least partially subject specific, such that different subjects may respond differently to the same type of anti-VEGF drug. Further, anti-VEGF therapies are typically administered via intravitreal injections, which can be expensive and themselves cause complications (e.g., blindness).

SUMMARY

In one or more embodiments, a method for predicting a treatment outcome is provided. Three-dimensional imaging data for a retina of a subject is received. A first output is generated using a deep learning system and the three-dimensional imaging data. The first output and baseline data are received as input for a symbolic model. A treatment outcome is predicted, via the symbolic model, for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

In one or more embodiments, a method for predicting a treatment outcome for a subject undergoing a treatment for neovascular age-related macular degeneration (nAMD). A first predicted outcome is generated using a deep learning system and three-dimensional imaging data for a retina of the subject. A second predicted outcome is generated using a symbolic model and baseline data for the subject. The treatment outcome is predicted for the subject undergoing the treatment for nAMD using the first predicted outcome and the second predicted outcome.

In one or more embodiments, a system for managing an anti-vascular endothelial growth factor (anti-VEGF) treatment for a subject diagnosed with neovascular age-related macular degeneration (nAMD) comprises a memory containing machine readable medium comprising machine executable code and a processor coupled to the memory. The processor configured to execute the machine executable code to cause the processor to: receive three-dimensional imaging data for a retina of a subject; generate a first output using a deep learning system and the three-dimensional imaging data; receive the first output and baseline data as input for a symbolic model; and predict, via the symbolic model, a treatment outcome for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

In some embodiments, a system is provided that includes one or more data processors and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods disclosed herein.

In some embodiments, a computer-program product is provided that is tangibly embodied in a non-transitory machine-readable storage medium and that includes instructions configured to cause one or more data processors to perform part or all of one or more methods disclosed herein.

Thus, it should be understood that although the present invention as claimed has been specifically disclosed by embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

DETAILED DESCRIPTION

Determining a subject's response to an age-related. macular degeneration (AMD) treatment and, in many cases, in particular, to a neovascular AMD (nAMD) treatment, may include determining the subjects visual acuity response, the subject's reduction in fovea! thickness, or both. A subject's visual acuity may be the sharpness of his or her vision, which may be measured by the subject's ability to discern letters or numbers at a given distance. Visual acuity is oftentimes ascertained via an eye exam and measured according to the standard Snellen eye chart. Retinal images may provide information that can be used to estimate a subject's visual acuity. For example, optical coherence tomography (OCT) images nay be used to estimate a subject's visual acuity at the time the OCT images were captured. Foveal thickness, which is also referred to as central subfield thickness (CST), may be defined as the average thickness of the macula in the central 1 mm diameter area. CST may also be measured using OCT images.

But in certain cases, such as, for example, in clinical trials, being able to predict a subject's future response to an AMI) treatment (e.g., nAMD treatment) may be desirable. For example, it may be desirable to predict whether a subject's visual acuity will have improved at a selected period of time after treatment (e.g., at 6 months after treatment, 9 months after treatment, at 12 months after treatment, at 24 months after treatment, etc.). Further, it may be desirable to classify any such improvement in visual acuity. In some cases, it may be desirable to predict whether a subject will experience a reduction in CST (e.g., any reduction in CST or a reduction greater than a selected threshold). Such predictions and classification may enable treatment regimens to be personalized for a given subject. For example, predictions about a subject's visual acuity response to a particular AMD treatment may be used to customize the injection dosage, the intervals at which injections are given, or both. Further, such predictions may improve clinical trial screening, prescreening, or both by enabling; the exclusion of those subjects predicted to not respond well to treatment.

Thus, the various embodiments described herein provide methods and systems for predicting treatment outcomes for subjects in response to AMD treatment (e.g., nAMD treatment). In particular, baseline data is input into a symbolic model and used to predict an outcome for a subject undergoing such a treatment. The outcome may include, for example, without limitation, a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, a predicted reduction in central subfield thickness, or a combination thereof. In some embodiments, the input sent into the symbolic model includes both the baseline data and an output (e.g., a previously generated predicted outcome) generated based on three-dimensional imaging data (e.g., OCT imaging data). For example, OCT imaging data may be processed via a deep learning system to generate a predicted outcome that is combined with the baseline data. In this manner, the baseline data and this predicted outcome are fused to form an input that is sent into the symbolic model.

In other embodiments, the symbolic model may be used to generate a first output using the baseline data and the deep learning system is used to generate a second output using the three-dimensional imaging data. These two outputs are combined, fused, or otherwise integrated to form an outcome output that includes or indicates a predicted treatment outcome. For example, the first output and the second output may be a first predicted outcome and a second predicted outcome, respectively. A weighted average (e.g., equally weighted average) of these two predicted outcomes may be used as the final treatment outcome for a subject.

Recognizing and taking into account the importance and utility of a methodology and system that can provide the improvements described above, the embodiments described herein provide methods and systems for predicting visual acuity response to an AMD treatment (e.g., nAMD treatment). More particularly, the embodiments described herein provide methods and systems for processing baseline data using a symbolic model to predict treatment outcomes in subjects undergoing nAMD treatment at a selected period of time (e.g., 6 months, 9 months, 12 months, 24 months, etc.) after a baseline point in time. The baseline point in time may be, for example, but is not limited to, day one of treatment. Using the methods and systems described herein may have the technical effect of reducing the overall computing resources and/or time needed to predict treatment outcomes in subjects undergoing nAMD treatment. Further, using the methods and systems may allow treatment outcomes in subjects to be predicted more efficiently and accurately as compared to other methods and systems.

Moreover, the embodiments described herein may facilitate the creation of personalized treatment regimens for individual subjects to ensure the proper dosage and/or intervals between treatment doses (e.g., injections). In particular, the embodiments described herein may help generate accurate, efficient, and expedient personalized treatment or dosing schedules and enhance clinical cohort selection or clinical trial design.

II.A. Exemplary Prediction System for Predicting AMD Treatment Outcomes

FIG.1is a block diagram of a prediction system100in accordance with various embodiments. Prediction system100is used to predict a treatment outcome for one or more subjects with respect to an AMD treatment. The AMD treatment, which may be an nAMD treatment, may include, for example, but is not limited to, an anti-VEGF treatment, an antibody treatment, another type of treatment, or a combination thereof. The anti-VEGF treatment may include, for example, ranibizumab, which may be administered via intravitreal injection. The antibody treatment may be, for example, a monoclonal antibody treatment that targets the vascular endothelial growth factor (VEGF) and angiopoietin2inhibitor. In one or more embodiments, the antibody treatment includes faricimab.

Prediction system100includes computing platform102, data storage104, and display system106. Computing platform102may take various forms. In one or more embodiments, computing platform102includes a single computer (or computer system) or multiple computers in communication with each other. In other examples, computing platform102takes the form of a cloud computing platform. In some examples, computing platform102takes the form of a mobile computing platform (e.g., a smartphone, a tablet, a smartwatch, etc.).

Data storage104and display system106are each in communication with computing platform102. In some examples, data storage104, display system106, or both may be considered part of or otherwise integrated with computing platform102. Thus, in some examples, computing platform102, data storage104, and display system106may be separate components in communication with each other, but in other examples, some combination of these components may be integrated together.

Prediction system100includes data analyzer108, which may be implemented using hardware, software, firmware, or a combination thereof. In one or more embodiments, data analyzer108is implemented in computing platform102. Data analyzer108processes a set of inputs110using model system112to predict (or generate) outcome output114.

Model system112may include any number of or combination of artificial intelligence models or machine learning models. In one or more embodiments, model system112includes a first outcome predictor model116and a second outcome predictor model118. In one or more embodiments, first outcome predictor model116includes a deep learning system, which may include, for example, one or more neural networks, with at least one of these one or more neural networks being a deep learning neural network (or deep neural network) (DNN). In one or more embodiments, second outcome predictor model118includes a symbolic model, the symbolic model including one or more models that use symbolic learning or symbolic reasoning. For example, second outcome predictor model118may include, without limitation, at least one of a linear model, a random forest model, an Extreme Gradient Boosting (XGBoost) algorithm, or another type of model or algorithm.

In one or more embodiments, set of inputs110sent into model system112may be at least partially received from a source external to prediction system100over one or more communications links (e.g., wired communications links, wireless communications links, optical communications links, etc.). In one or more embodiments, set of inputs110is at least partially retrieved from data storage104.

Set of inputs110for model system112may include, baseline data120. In one or more embodiments, set of inputs110may additionally include three-dimensional imaging data122. Baseline data120includes data obtained for a baseline point in time. The baseline point in time may be, for example, a point in time prior to treatment or a point in time concurrent with a first dose of a treatment (e.g., day one of treatment).

Baseline data120may include, for example, without limitation, at least one of demographic data, a baseline visual acuity measurement, a baseline CST measurement, a baseline low-luminance deficit (LLD), a treatment arm, or some other type of baseline measurement. The demographic data may include, for example, without limitation, at least one of age, gender, or another type of demographic metric. The baseline visual acuity measurement may be, for example, a best corrected visual acuity (BCVA) measurement. The baseline CST measurement may be, for example, in micrometers. The LLD may be the difference between a baseline BCVA measurement and a baseline low-luminance visual acuity (LLVA) measurement.

Three-dimensional imaging data122may include OCT imaging data, data extracted from OCT images (e.g., OCT en-face images), tabular data extracted from OCT images, some other form of imaging data, or a combination thereof. The OCT imaging data may include, for example, spectral domain OCT (SD-OCT) B-scans. Three-dimensional imaging data122may be imaging data for a baseline point in time for the subject prior to treatment or concurrent with a first dose of a treatment.

Model system112processes set of inputs110to predict at least one treatment outcome124for a subject who has or will undergo an nAMD treatment. Treatment outcome124may include, for example, without limitation, at least one of a predicted visual acuity measurement (e.g., a predicted BCVA), a predicted changed in visual acuity (e.g., a predicted change in BCVA), a predicted CST, a predicted reduction in CST, or some other type of treatment outcome of a subject undergoing treatment. Treatment outcome124may be generated for a selected point in time after a baseline point in time. For example, treatment outcome124may be predicted at an nthmonth after a baseline point in time, the nth month being selected as a month between three months and thirty months after the baseline point in time. In one or more embodiments, treatment outcome124may be predicted for a time such as, without limitation, 6 months, 9 months, 12 months, 24 months, or some other amount of time after treatment. Examples of how model system112can be used to predict treatment outcome124are described in greater detail inFIGS.2-4below.

Data analyzer108may use treatment outcome124to form outcome output114. Outcome output114may include, for example, treatment outcome124. In one or more embodiments, outcome output114includes multiple treatment outcomes for multiple points in time after treatment (e.g., a treatment outcome for 6 months, a treatment outcome for 9 months, and a treatment outcome for 12 months).

In one or more embodiments, outcome output114includes other information generated based on treatment outcome124. For example, outcome output114may include a personalized treatment regimen for a given subject based on the predicted treatment outcome124. In some examples, outcome output114may include a customized injection dosage, one or more intervals at which injections are to be given, or both. Outcome output114may include, in some cases, an indication to change or supplement the type of treatment to be administered to the subject based on the predicted treatment outcome124indicating that the subject will not have a desired response to the treatment. In this manner, outcome output114may be used to improve overall treatment management.

In one or more embodiments, at least a portion of outcome output114or a graphical representation of at least a portion of outcome output114is displayed on display system106. In some embodiments, at least a portion of outcome output114or a graphical representation of at least a portion of outcome output114is sent to remote device126(e.g., a mobile device, a laptop, a server, a cloud, etc.).

II.B. Exemplary Methodologies for Predicting AMD Treatment Outcomes

FIG.2is a flowchart of a process200for predicting a treatment outcome in accordance with various embodiments. In one or more embodiments, process200is implemented using prediction system100described inFIG.1.

Step202includes receiving three-dimensional imaging data for a retina of a subject. Three-dimensional imaging data122inFIG.1may be one example of an implementation for the three-dimensional imaging data in step202. The three-dimensional imaging data may include OCT imaging data, data extracted from OCT images (e.g., OCT en-face images), tabular data extracted from OCT images, some other form of imaging data, or a combination thereof. The OCT imaging data may include, for example, spectral domain OCT (SD-OCT) B-scans. The three dimensional imaging data may be imaging data for a baseline point in time for the subject prior to treatment or concurrent with a first dose of a treatment.

Step204includes generating a first output using a deep learning system and the three-dimensional imaging data. First outcome predictor model116described inFIG.1may be one example of an implementation for the deep learning system used in step204. The deep learning system may be comprised of one or more neural networks. In one or more embodiments, the first output generated in step204is a predicted outcome (e.g., a predicted treatment outcome). For example, the deep learning system may have been trained to predict a treatment outcome based on one or more OCT images generated at a baseline point in time for the subject.

Step206includes receiving the first output and baseline data as input for a symbolic model. Second outcome predictor model118described inFIG.1may be one example of an implementation for the symbolic model used in step206. The symbolic model may be implemented using, for example, at least one of a linear model, a random forest model, an XGBoost algorithm, or another type of symbolic learning model. Baseline data120inFIG.1may be one example of an implementation for the baseline data in step206. The baseline data may include, for example, at least one of demographic data (e.g., age, gender, etc.), a baseline visual acuity measurement (e.g., a baseline BCVA), a baseline central subfield thickness (CST) measurement, a baseline low-luminance deficit (LLD), or a treatment arm.

Step208includes predicting (or generating), via the symbolic model, a treatment outcome for a subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input. The treatment outcome may include, for example, without limitation, at least one of a predicted visual acuity measurement (e.g., a predicted BCVA), a predicted change in visual acuity, a predicted CST, a predicted reduction in CST, or another indicator of the response of a subject to the treatment. The treatment outcome predicted in step208may be for a selected point in time after treatment such as, for example, without limitation, 6 months, 9 months, 12 months, 24 months, or some other amount of time after treatment.

In various embodiments, the treatment outcome predicted (or generated) in step208includes a visual acuity response (VAR) output that is a value or score that identifies the predicted change in the visual acuity of the subject. For example, the VAR output may be a value or score that classifies the subject's visual acuity response with respect to the level of improvement predicted (e.g., letters of improvement) or decline (e.g., vision loss). As one specific example, the VAR output may be a predicted numeric change in BCVA that is later processed and identified as belonging to one of a plurality of different classes of BCVA change, each class of BCVA change corresponding to a different range of letters of improvement. As another example, the VAR output may be the predicted class of change itself. In still other examples, the VAR output may be a predicted change in some other measure of visual acuity. In other embodiments, the VAR output may be a value or representational output that requires one or more additional processing steps to arrive at the predicted change in visual acuity. For example, the VAR output may be a predicted, future BCVA of the subject at a period of time post-treatment (e.g., at 9 months, at 12 months). The additional one or more processing steps may include computing the difference between the predicted, future BCVA and the baseline BCVA to determine the predicted change in visual acuity.

Process200may optionally include step210. Step210includes generating an outcome output based on the treatment outcome. Outcome output114inFIG.1may be one example of an implementation for the outcome output in step210. The outcome output may include, for example, the treatment outcome or multiple treatment outcomes for multiple points in time after treatment (e.g., a treatment outcome for 6 months, a treatment outcome for 9 months, and a treatment outcome for 12 months).

In one or more embodiments, the outcome output includes other information generated based on the treatment outcome. For example, the outcome output may include a personalized treatment regimen for a given subject based on the predicted treatment outcome. In some examples, the outcome output may include a customized injection dosage, one or more intervals at which injections are to be given, or both. The outcome output may include, in some cases, an indication to change or supplement the type of treatment to be administered to the subject based on the predicted treatment outcome indicating that the subject will not have a desired response to the treatment. In this manner, the outcome output may be used to improve overall treatment management.

FIG.3is a flowchart of a process300for predicting a treatment outcome in accordance with various embodiments. In one or more embodiments, process300is implemented using prediction system100described inFIG.1.

Step302includes generating a first output using a deep learning system and three-dimensional imaging data of a retina of a subject. Three-dimensional imaging data122inFIG.1may be one example of an implementation for the three-dimensional imaging data in step302. The three-dimensional imaging data may include OCT imaging data, data extracted from OCT images (e.g., OCT en-face images), tabular data extracted from OCT images, some other form of imaging data, or a combination thereof. The OCT imaging data may include, for example, spectral domain OCT (SD-OCT) B-scans. The three dimensional imaging data may be imaging data for a baseline point in time for the subject prior to treatment or concurrent with a first dose of a treatment.

The first output in step302may include a first predicted outcome (a first predicted treatment outcome). For example, the deep learning system may be trained to generate the first predicted outcome based on the three-dimensional imaging data.

Step304includes generating a second output using a symbolic model and baseline data. Baseline data120inFIG.1may be one example of an implementation for the baseline data in step304. The baseline data may include, for example, at least one of demographic data (e.g., age, gender, etc.), a baseline visual acuity measurement (e.g., a baseline BCVA), a baseline central subfield thickness (CST) measurement, a baseline low-luminance deficit (LLD), or a treatment arm.

The second output in step304may include a second predicted outcome (a second predicted treatment outcome). For example, the symbolic model may be trained to generate the second predicted outcome based on the baseline data. Second outcome predictor model118described inFIG.1may be one example of an implementation for the symbolic model used in step304. The symbolic model may be implemented using, for example, at least one of a linear model, a random forest model, an XGBoost algorithm, or another type of symbolic learning model.

Step306includes predicting the treatment outcome for the subject undergoing the treatment for nAMD using the first output and the second output. In one or more embodiments, step306includes predicting the treatment outcome as a weighted average (e.g., equally weighted average) of the first output (e.g., the first predicted outcome) and the second output (e.g., the second predicted outcome). In some embodiments, the first predicted outcome generated by the deep learning system may be weighted greater than the second predicted outcome generated by the symbolic model. In other embodiments, the second predicted outcome generated by the symbolic model may be weighted greater than the first predicted outcome generated by the deep learning system.

Process300may optionally include step308. Step308may include generating an outcome output based on the treatment outcome. Outcome output114inFIG.1may be one example of an implementation for the outcome output in step308. The outcome output may include, for example, the treatment outcome or multiple treatment outcomes for multiple points in time after treatment (e.g., a treatment outcome for 6 months, a treatment outcome for 9 months, and a treatment outcome for 12 months).

In one or more embodiments, the outcome output includes other information generated based on the treatment outcome. For example, the outcome output may include a personalized treatment regimen for a given subject based on the predicted treatment outcome. In some examples, the outcome output may include a customized injection dosage, one or more intervals at which injections are to be given, or both. The outcome output may include, in some cases, an indication to change or supplement the type of treatment to be administered to the subject based on the predicted treatment outcome indicating that the subject will not have a desired response to the treatment. In this manner, the outcome output may be used to improve overall treatment management.

FIG.4is a flowchart of a process400for predicting a treatment outcome in accordance with various embodiments. In one or more embodiments, process400is implemented using prediction system100described inFIG.1.

Step402includes receiving baseline data as an input for a symbolic model. Baseline data120inFIG.1may be one example of an implementation for the baseline data in step402. Further, second outcome predictor model118described inFIG.1may be one example of an implementation for the symbolic model used in step206. The symbolic model may be implemented using, for example, at least one of a linear model, a random forest model, an XGBoost algorithm, or another type of symbolic learning model. In one or more embodiments, the baseline data includes a baseline visual acuity measurement (e.g., a baseline BCVA). This baseline visual acuity measurement may have been generated using three-dimensional imaging data (e.g., OCT imaging data) and a deep learning system.

Step404includes processing the baseline data using the symbolic model. The symbolic model may use any number of symbolic artificial intelligence learning methodologies to process the baseline data. In some embodiments, step404includes processing the baseline data and a previously generated treatment outcome received from another system (e.g., a deep learning system).

Step406includes predicting, via the symbolic model, a treatment outcome for a subject undergoing a treatment for nAMD based on the processing of the baseline data. Treatment outcome124inFIG.1may be one example of an implementation for the treatment outcome.

III. Exemplary Experimental Data

A first study was conducted using data from185eyes treated with an nAMD treatment (e.g., faricimab). This data was obtained for subjects from the AVENUE clinical trial (NCT02484690) who were randomized into four faricimab treatment arms. The data for a particular eye included baseline data and post-treatment data. The baseline data included demographic data (age, gender), a baseline BCVA, a baseline CST, a low-luminance deficit, and treatment arm. The data further included SD-OCT imaging data (e.g., B scans) of the eyes. The post-treatment data included complete BCVA data, CST at month 9 after treatment. The data was split into 80% training data and 20% testing data.

Treatment outcomes were predicted using a deep learning system (e.g., an example of an implementation for first outcome predictor model116inFIG.1) and various symbolic models (e.g., examples of implementations for second outcome predictor model118inFIG.1). Treatment outcomes were defined in two ways: functional and anatomical. The functional portion of a treatment outcome included a VAR output (e.g., a BCVA letter score at month 9). The anatomical portion of the treatment outcome included a CST reduction rate from the baseline point in time to month 9, with the CST reduction rate being converted into a binary true/false variable (e.g., with true indicating a CST reduction rate greater than 35%). The threshold (e.g., 35%) for the binary variable was selected based on an average or median CST reduction rate for the subjects.

The primary metric for the functional portion of the treatment outcome was a coefficient of determination (R2) score. The primary metric for the anatomical portion of the treatment outcome was an area under the receiver operator characteristic (AUROC) curve. Secondary metrics included accuracy, precision, and recall. Evaluation of model performance included 5-fold cross validation.

In a model stacking approach comprising two stages involving a given symbolic model, the deep learning system was first used to generate a predicted outcome in a first stage. This predicted outcome was then used as one of the input features, along with the baseline data, for the symbolic model in a second stage. 5-fold cross validation was used to tune hyper-parameters of the deep learning system and the symbolic model. For the first stage, 5-fold CV was used to tune hyper-parameters of the deep learning system. In iteration i (i=1, 2, 3, 4, 5) of the second stage 5-fold cross validation, the prediction of the deep learning system from iteration i of the first stage 5-fold cross validation was used as one of the input features in combination with the baseline data. Six total models were developed using the model stacking approach.

In a model averaging approach, for a given symbolic model, the predicted outcome generated by the deep learning system and the predicted outcome generated by the symbolic model were averaged together (e.g., via equal weighting) to generate the predicted treatment outcome. Six total models were developed using the model averaging approach.

To calculate test data performance metrics, the symbolic model was retrained on the entire training data set with the optimal hyper-parameters found in 5-fold cross validation. The deep learning system was used in an ensemble way, that is, the average of the five deep learning systems (i.e., from each 5-fold CV iteration) was used.

FIG.5is a table showing the performance data for a model stacking and model averaging approach in predicting a treatment outcome in accordance with one or more embodiments. The treatment outcome includes a predicted BCVA at month 9. The benchmark models identify each individual model that was used. With respect to model stacking, the identified model is the symbolic model that was stacked with the deep learning system. With respect to model averaging, the identified model is the symbolic model whose output was averaged with the output of the deep learning system.

FIG.6is a table showing the performance data for a model stacking and model averaging approach in predicting a treatment outcome in accordance with one or more embodiments. The treatment outcome includes a CST reduction rate classification where a true or positive classification indicates a CST reduction rate of greater than 35%. The benchmark models identify each individual model that was used. With respect to model stacking, the identified model is the symbolic model that was stacked with the deep learning system. With respect to model averaging, the identified model is the symbolic model whose output was averaged with the output of the deep learning system.

FIG.7is a block diagram illustrating an example of a computer system in accordance with various embodiments. Computer system700may be an example of one implementation for computing platform102described above inFIG.1. In one or more examples, computer system700can include a bus702or other communication mechanism for communicating information, and a processor704coupled with bus702for processing information. In various embodiments, computer system700can also include a memory, which can be a random-access memory (RAM)706or other dynamic storage device, coupled to bus702for determining instructions to be executed by processor704. Memory also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor704. In various embodiments, computer system700can further include a read-only memory (ROM)708or other static storage device coupled to bus702for storing static information and instructions for processor704. A storage device710, such as a magnetic disk or optical disk, can be provided and coupled to bus702for storing information and instructions.

In various embodiments, computer system700can be coupled via bus702to a display712, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device714, including alphanumeric and other keys, can be coupled to bus702for communicating information and command selections to processor704. Another type of user input device is a cursor control716, such as a mouse, a joystick, a trackball, a gesture-input device, a gaze-based input device, or cursor direction keys for communicating direction information and command selections to processor704and for controlling cursor movement on display712. This input device714typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. However, it should be understood that input devices714allowing for three-dimensional (e.g., x, y and z) cursor movement are also contemplated herein.

Consistent with certain implementations of the present teachings, results can be provided by computer system700in response to processor704executing one or more sequences of one or more instructions contained in RAM706. Such instructions can be read into RAM706from another computer-readable medium or computer-readable storage medium, such as storage device710. Execution of the sequences of instructions contained in RAM706can cause processor704to perform the processes described herein. Alternatively, hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

The term “computer-readable medium” (e.g., data store, data storage, storage device, data storage device, etc.) or “computer-readable storage medium” as used herein refers to any media that participates in providing instructions to processor704for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, optical, solid state, magnetic disks, such as storage device710. Examples of volatile media can include, but are not limited to, dynamic memory, such as RAM706. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus702.

In addition to computer readable medium, instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor704of computer system700for execution. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein. Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, optical communications connections, etc.

It should be appreciated that the methodologies described herein, flow charts, diagrams, and accompanying disclosure can be implemented using computer system700as a standalone device or on a distributed network of shared computer processing resources such as a cloud computing network.

In various embodiments, the methods of the present teachings may be implemented as firmware and/or a software program and applications written in conventional programming languages such as C, C++, Python, etc. If implemented as firmware and/or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above. It should be understood that the various engines described herein can be provided on a computer system, such as computer system700, whereby processor704would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, the memory components RAM706, ROM,708, or storage device710and user input provided via input device714.

V. Exemplary Definitions and Context

The disclosure is not limited to these exemplary embodiments and applications or to the manner in which the exemplary embodiments and applications operate or are described herein. Moreover, the figures may show simplified or partial views, and the dimensions of elements in the figures may be exaggerated or otherwise not in proportion.

In addition, as the terms “on,” “attached to,” “connected to,” “coupled to,” or similar words are used herein, one element (e.g., a component, a material, a layer, a substrate, etc.) can be “on,” “attached to,” “connected to,” or “coupled to” another element regardless of whether the one element is directly on, attached to, connected to, or coupled to the other element or there are one or more intervening elements between the one element and the other element. In addition, where reference is made to a list of elements (e.g., elements a, b, c), such reference is intended to include any one of the listed elements by itself, any combination of less than all of the listed elements, and/or a combination of all of the listed elements. Section divisions in the specification are for ease of review only and do not limit any combination of elements discussed.

The term “subject” may refer to a subject of a clinical trial, a person undergoing treatment, a person undergoing anti-cancer therapies, a person being monitored for remission or recovery, a person undergoing a preventative health analysis (e.g., due to their medical history), or any other person or patient of interest. In various cases, “subject” and “patient” may be used interchangeably herein.

Unless otherwise defined, scientific and technical terms used in connection with the present teachings described herein shall have the meanings that are commonly understood by those of ordinary skill in the art. Further, unless otherwise indicated by context, singular terms shall include pluralities and plural terms shall include the singular. Generally, nomenclatures utilized in connection with, and techniques of, chemistry, biochemistry, molecular biology, pharmacology and toxicology are described herein are those well-known and commonly used in the art.

As used herein, “substantially” means sufficient to work for the intended purpose. The term “substantially” thus allows for minor, insignificant variations from an absolute or perfect state, dimension, measurement, result, or the like such as would be expected by a person of ordinary skill in the field but that do not appreciably affect overall performance. When used with respect to numerical values or parameters or characteristics that can be expressed as numerical values, “substantially” may mean within ten percent.

The term “ones” means more than one.

As used herein, the term “plurality” can be2,3,4,5,6,7,8,9,10, or more.

As used herein, the term “set of” means one or more. For example, a set of items includes one or more items.

As used herein, the phrase “at least one of,” when used with a list of items, means different combinations of one or more of the listed items may be used and, in some cases, only one of the items in the list may be used. The item may be a particular object, thing, step, operation, process, or category. In other words, “at least one of” means any combination of items or number of items may be used from the list, but not all of the items in the list may be used. For example, without limitation, “at least one of item A, item B, or item C” means item A; item A and item B; item B; item A, item B, and item C; item B and item C; or item A and C. In some cases, “at least one of item A, item B, or item C” means, but is not limited to, two of item A, one of item B, and ten of item C; four of item B and seven of item C; or some other suitable combination.

As used herein, a “model” may include one or more algorithms, one or more mathematical techniques, one or more machine learning algorithms, or a combination thereof.

As used herein, “machine learning” may be the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. Machine learning uses algorithms that can learn from data without relying on rules-based programming.

A neural network may process information in two ways. For example, it may process information when it is being trained in training mode and when it puts what it has learned into practice in inference (or prediction) mode. Neural networks may learn through a feedback process (e.g., backpropagation) which allows the network to adjust the weight factors (modifying its behavior) of the individual nodes in the intermediate hidden layers so that the output matches the outputs of the training data. In other words, a neural network may learn by being fed training data (learning examples) and eventually learns how to reach the correct output, even when it is presented with a new range or set of inputs. A neural network may include, for example, without limitation, at least one of a Feedforward Neural Network (FNN), a Recurrent Neural Network (RNN), a Modular Neural Network (MNN), a Convolutional Neural Network (CNN), a Residual Neural Network (ResNet), an Ordinary Differential Equations Neural Networks (neural-ODE), or another type of neural network.

VI. Recitation of Embodiments

Embodiment 1. A method for predicting a treatment outcome, the method comprising: receiving three-dimensional imaging data for a retina of a subject; generating a first output using a deep learning system and the three-dimensional imaging data; receiving the first output and baseline data as input for a symbolic model; and predicting, via the symbolic model, a treatment outcome for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

Embodiment 2. The method of embodiment 1, wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data.

Embodiment 3. The method of embodiment 1 or embodiment 2, wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement, a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.

Embodiment 4. The method of embodiment 3, wherein the demographic data comprises at least one of age or gender.

Embodiment 5. The method of any one of embodiments 1-4, wherein the treatment outcome includes at least one of a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.

Embodiment 6. The method of any one of embodiments 1-5, wherein the baseline data includes a baseline visual acuity measurement and further comprising:identifying the baseline visual acuity measurement using the first output.

Embodiment 7. The method of any one of embodiments 1-6, wherein the treatment outcome is predicted at an nthmonth after a baseline point in time and wherein the nthmonth is selected as a month between three months and thirty months after the baseline point in time.

Embodiment 8. The method of any one of embodiments 1-7, wherein the treatment comprises a monoclonal antibody that targets vascular endothelial growth factor, and angiopoietin 2 inhibitor.

Embodiment 9. The method of any one of embodiments 1-8, wherein the treatment comprises faricimab.

Embodiment 10. A method for predicting a treatment outcome for a subject undergoing a treatment for neovascular age-related macular degeneration (nAMD), the method comprising: generating a first predicted outcome using a deep learning system and three-dimensional imaging data for a retina of the subject; generating a second predicted outcome using a symbolic model and baseline data for the subject; and predicting the treatment outcome for the subject undergoing the treatment for nAMD using the first predicted outcome and the second predicted outcome.

Embodiment 11. The method of embodiment 10, wherein the predicting comprises: predicting the treatment outcome as a weighted average of the first predicted treatment outcome and the second predicted treatment outcome.

Embodiment 12. The method of embodiment 10 or embodiment 11, wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data.

Embodiment 13. The method of any one of embodiments 10-12, wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement, a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.

Embodiment 14. The method of embodiment 13, wherein the demographic data comprises at least one of age or gender.

Embodiment 15. The method of any one of embodiments 10-14, wherein each of the first predicted treatment outcome, the second predicted treatment outcome, and the treatment outcome includes at least one of a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.

Embodiment 16. A system for managing an anti-vascular endothelial growth factor (anti-VEGF) treatment for a subject diagnosed with neovascular age-related macular degeneration (nAMD), the system comprising: a memory containing machine readable medium comprising machine executable code; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to:receive three-dimensional imaging data for a retina of a subject; generate a first output using a deep learning system and the three-dimensional imaging data;receive the first output and baseline data as input for a symbolic model; andpredict, via the symbolic model, a treatment outcome for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

Embodiment 17. The system of embodiment 16, wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data.

Embodiment 18. The system of embodiment 16 or embodiment 17, wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement, a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.

Embodiment 19. The system of any one of embodiments 16-18, wherein the treatment outcome includes at least one of a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.

Embodiment 20. The system of any one of embodiments 16-18, wherein the treatment comprises faricimab.

Embodiment 21. A method for predicting a treatment outcome, the method comprising: receiving baseline data as an input for a symbolic model; processing the baseline data using the symbolic model; and predicting, via the symbolic model, an outcome for a subject undergoing a treatment based on the processing of the baseline data.

Embodiment 22. The method of embodiment 21, wherein the baseline data includes a baseline visual acuity measurement and further comprising: generating the baseline visual acuity measurement using three-dimensional imaging data and a deep learning system.

VII. Additional Considerations

The headers and subheaders between sections and subsections of this document are included solely for the purpose of improving readability and do not imply that features cannot be combined across sections and subsection. Accordingly, sections and subsections do not describe separate embodiments.

While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those of skill in the art. In describing the various embodiments, the specification may have presented a method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the various embodiments. It is understood that various changes may be made in the function and arrangement of elements (e.g., elements in block or schematic diagrams, elements in flow diagrams, etc.) without departing from the spirit and scope as set forth in the appended claims.