# US 12,002,559 B2 · Discovery platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,559 B2 - Document ID: US12002559B2 - Status: Granted - Kind: B2 - Application number: 18/336,905 - Filed: 2023-06-16 - Published: 2023-11-09 - Granted: 2024-06-04 - Earliest claimed: 2021-08-16 - Assignees: Insitro, Inc. - Applicants: Insitro, Inc. - Inventors: Francesco Paolo Casale; Michael Bereket; Matthew Albert - Web page: https://patentgrep.com/patent/US12002559B2 ## CPC - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication} - G06T 7/0012: {Biomedical image inspection} - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 50/50: for simulation or modelling of medical disorders - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest. ## Claims ### Claim 1 A system for evaluating a treatment with respect to progression of a disease of interest, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: obtaining a plurality of baseline placebo medical images of a placebo group of subjects captured before a placebo is administered to the placebo group of subjects and a plurality of follow-up placebo medical images of the placebo group of subjects captured after the placebo is administered to the placebo group of subjects; generating a plurality of baseline placebo embeddings in a latent space based on the plurality of baseline placebo medical images; generating a plurality of follow-up placebo embeddings in the latent space based on the plurality of follow-up placebo medical images; inputting the plurality of baseline placebo embeddings into one or more machine learning models to obtain a plurality of predicted follow-up placebo embeddings in the latent space; obtaining a plurality of placebo progression embeddings based on the plurality of follow-up placebo embeddings and the plurality of predicted follow-up placebo embeddings; obtaining a plurality of baseline treatment medical images of a treatment group of subjects captured before the treatment is administered to the treatment group of subjects and a plurality of follow-up treatment medical images of the treatment group of subjects captured after the treatment is administered to the treatment group of subjects; generating a plurality of treatment placebo embeddings in the latent space based on the plurality of baseline treatment medical images; generating a plurality of follow-up treatment embeddings in the latent space based on the plurality of follow-up treatment medical images; inputting the plurality of baseline treatment embeddings into the one or more machine learning models to obtain a plurality of predicted follow-up treatment embeddings in the latent space; obtaining a plurality of treatment progression embeddings based on the plurality of follow-up treatment embeddings and the plurality of predicted follow-up treatment embeddings; and generating a classification model for determining whether a patient has received the placebo or the treatment based on the plurality of treatment progression embeddings and the plurality of placebo progression embeddings. ### Claim 2 (depends on 1) The system of claim 1, wherein outputs of the classification model are indicative of drug response phenotype. ### Claim 3 (depends on 1) The system of claim 1, wherein the one or more programs further include instructions for: determining, based on the classification model, a correlation metric between the treatment and the progression of the disease of interest. ### Claim 4 (depends on 3) The system of claim 3, wherein the correlation metric is a P value. ### Claim 5 (depends on 3) The system of claim 3, wherein the one or more programs further include instructions for: comparing the correlation metric with a predefined threshold. ### Claim 6 (depends on 5) The system of claim 5, wherein the one or more programs further include instructions for: identifying, based on the comparison, an association between the treatment and progression of the disease of interest. ### Claim 7 (depends on 6) The system of claim 6, wherein the one or more programs further include instructions for: prescribing the treatment in a new subject based on the association. ### Claim 8 (depends on 6) The system of claim 6, wherein the one or more programs further include instructions for: administering the treatment based on the association. ### Claim 9 (depends on 6) The system of claim 6, wherein the one or more programs further include instructions for: adjusting the treatment based on the association. ### Claim 10 (depends on 6) The system of claim 6, wherein the one or more programs further include instructions for: providing a medical recommendation based on the association. ### Claim 11 (depends on 6) The system of claim 6, wherein the one or more programs further include instructions for: generating a report based on the association. ### Claim 12 (depends on 1) The system of claim 1, wherein the disease of interest is non-alcoholic steatohepatitis (NASH). ### Claim 13 (depends on 1) The system of claim 1, wherein: the plurality of baseline placebo embeddings are generated by inputting the plurality of baseline placebo medical images into a trained unsupervised machine-learning model; the plurality of follow-up placebo embeddings are generated by inputting the plurality of follow-up placebo medical images into the trained unsupervised machine-learning model; and the plurality of placebo progression embeddings are determined by calculating differences between the plurality of follow-up placebo embeddings and the plurality of predicted follow-up placebo embeddings. ### Claim 14 (depends on 13) The system of claim 13, wherein the one or more machine learning models comprise a trained linear model. ### Claim 15 (depends on 14) The system of claim 14, wherein the trained linear model is configured to receive a baseline embedding and output a predicted follow-up embedding. ### Claim 16 (depends on 15) The system of claim 15, wherein the trained linear model is a linear mixed model. ### Claim 17 (depends on 15) The system of claim 15, wherein the placebo group of subjects is a first placebo group, and wherein the trained linear model is trained using medical image data from a second placebo group different from the first placebo group. ### Claim 18 (depends on 13) The system of claim 13, wherein the one or more machine learning models include the trained unsupervised machine-learning model. ### Claim 19 (depends on 13) The system of claim 13, wherein the plurality of baseline treatment embeddings are generated by inputting the plurality of baseline treatment medical images into the trained unsupervised machine-learning model; the plurality of follow-up treatment embeddings are generated by inputting the plurality of follow-up treatment medical images into the trained unsupervised machine-learning model; and the plurality of treatment progression embeddings are determined by calculating differences between the plurality of follow-up treatment embeddings and the plurality of predicted follow-up treatment embeddings. ### Claim 20 (depends on 13) The system of claim 13, wherein the trained unsupervised machine-learning model is a contrastive model. ### Claim 21 (depends on 20) The system of claim 20, wherein the contrastive model is a SimCLR model. ### Claim 22 (depends on 1) The system of claim 1, wherein the classification model is configured to receive an input progression embedding and output a classification result indicating whether a patient has received the placebo or the treatment. ### Claim 23 (depends on 1) The system of claim 1, wherein the plurality of baseline placebo medical images, the plurality of follow-up placebo medical images, the plurality of baseline treatment medical images, and the plurality of follow-up treatment images are biopsy medical images.