# US 12,763,179 B2 · Direct medical treatment predictions using artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,763,179 B2 - Document ID: US12763179B2 - Status: Granted - Kind: B2 - Application number: 18/757,298 - Filed: 2024-06-27 - Published: 2024-10-17 - Granted: 2026-10-13 - Earliest claimed: 2021-12-03 - Assignees: Digital Diagnostics Inc. - Applicants: Digital Diagnostics Inc. - Inventors: Michael D. Abramoff - Web page: https://patentgrep.com/patent/US12763179B2 ## CPC - G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06T 7/0012: {Biomedical image inspection} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment. ## Claims ### Claim 1 A method for autonomously predicting an efficacy of a treatment for a patient, the method comprising: receiving image data of an anatomy of a patient; obtaining scores for each of a plurality of anatomical features reflected in the image data by applying a feature extraction model that is a first machine learning model to the image data; applying the scores as input to a treatment model, the treatment model comprising a second machine learning model configured to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy; and receiving, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining, based on the image data, a body part to which the image data corresponds; and selecting the feature extraction model from a plurality of candidate feature models based on a concordance between each candidate feature model and a given body part. ### Claim 3 (depends on 1) The method of claim 1, wherein applying the scores as input to the treatment model comprises generating a feature vector that stores, for each anatomical feature of the plurality, its respective identification, and applying the feature vector as input to the treatment model. ### Claim 4 (depends on 1) The method of claim 1, further comprising: determining, for the particular treatment, whether its predicted measure of efficacy exceeds a threshold; and outputting to a user a recommendation for the particular treatment responsive to determining that its predicted measure of efficacy exceeds the threshold. ### Claim 5 (depends on 1) The method of claim 1, wherein receiving, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment comprises receiving, as output from the treatment model, data representative of respective measures of efficacy for each of a plurality of candidate treatments. ### Claim 6 A non-transitory computer-readable medium comprising instructions encoded thereon for autonomously determining a treatment for a patient, the instructions when executed causing one or more processors to perform operations, the instructions comprising instructions to: receive image data of an anatomy of a patient; obtain scores for each of a plurality of anatomical features reflected in the image data by applying a feature extraction model that is a first machine learning model to the image data; apply the scores as input to a treatment model, the treatment model comprising a second machine learning model configured to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy; and receive, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment. ### Claim 7 (depends on 6) The non-transitory computer-readable medium of claim 6, the instructions further comprising instructions to: determine, based on the image data, a body part to which the image data corresponds; and select the feature extraction model from a plurality of candidate feature models based on a concordance between each candidate feature model and a given body part. ### Claim 8 (depends on 6) The non-transitory computer-readable medium of claim 6, wherein applying the scores as input to the treatment model comprises generating a feature vector that stores, for each anatomical feature of the plurality, its respective score, and applying the feature vector as input to the treatment model. ### Claim 9 (depends on 6) The non-transitory computer-readable medium of claim 6, the instructions further comprising instructions to: determine, for the particular treatment, whether its predicted measure of efficacy exceeds a threshold; and output to a user a recommendation for the particular treatment responsive to determining that its predicted measure of efficacy exceeds the threshold. ### Claim 10 (depends on 6) The non-transitory computer-readable medium of claim 6, wherein receiving, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment comprises receiving, as output from the treatment model, data representative of respective measures of efficacy for each of a plurality of candidate treatments. ### Claim 11 A method for autonomously determining a treatment for a patient, the method comprising: receiving sensor data from an electronic device that monitors a patient; accessing a machine learning model, the machine learning model configured to output a likelihood that a particular treatment would yield a positive result, where the machine learning model is trained using training data that pairs previously obtained sensor data for a plurality of patients to labels describing whether the particular treatment yielded a positive result for each of the plurality of patients; applying the received sensor data to the machine learning model; and receiving, as output from the machine learning model, data representative of a likelihood of whether the patient would benefit from the particular treatment. ### Claim 12 (depends on 11) The method of claim 11, wherein the data representative of the likelihood of whether the patient would benefit from the particular treatment comprises a probability that the particular treatment would bring a benefit to the patient, and wherein the method further comprises: determining, for the particular treatment, whether its corresponding probability exceeds a threshold; and outputting to a user a recommendation for the particular treatment. ### Claim 13 (depends on 11) The method of claim 11, wherein the machine learning model is a convolutional neural network. ### Claim 14 (depends on 11) The method of claim 11, wherein the machine learning model is a multi-task model comprising a shared layer and branches, the shared layer trained to determine one or more candidate diagnoses based on the sensor data, each branch corresponding to a different treatment, each branch trained to output a likelihood that the different treatment to which the branch corresponds will be effective. ### Claim 15 (depends on 14) The method of claim 14, wherein an amount of training data for a branch corresponding to a given treatment is below a threshold, and wherein the multi-task model enriches the amount of training data by back-propagating the training data with information from a different branch. ### Claim 16 A non-transitory computer-readable medium comprising instructions encoded thereon for autonomously determining a treatment for a patient, the instructions when executed causing one or more processors to perform operations, the instructions comprising instructions to: receive sensor data from an electronic device that monitors a patient; access a machine learning model, the machine learning model configured to output a likelihood that a particular treatment would yield a positive result, where the machine learning model is trained using training data that pairs previously obtained sensor data for a plurality of patients to labels describing whether the particular treatment yielded a positive result for each of the plurality of patients; apply the received sensor data to the machine learning model; and receive, as output from the machine learning model, data representative of a likelihood of whether the patient would benefit from the particular treatment. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the data representative of the one or more treatments comprises probabilities that each of the one or more treatments would bring a benefit to the patient, and wherein the instructions further comprise instructions to: determine, for each of the one or more treatments, whether its corresponding probability exceeds a threshold; and output to a user a recommendation for each of the one or more treatments that has a corresponding probability that exceeds the threshold. ### Claim 18 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the machine learning model is a convolutional neural network. ### Claim 19 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the machine learning model is a multi-task model comprising a shared layer and branches, the shared layer trained to determine one or more candidate diagnoses based on the sensor data, each branch corresponding to a different treatment, each branch trained to output a likelihood that the different treatment to which the branch corresponds will be effective. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, wherein an amount of training data for a branch corresponding to a given treatment is below a threshold, and wherein the multi-task model enriches the amount of training data by back-propagating the training data with information from a different branch.