Patent ID: 11972870
Assignee: PATHOMIQ INC.
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 9:
10. A system of predicting patient response to therapy, comprising:
a patch generator configured to receive a plurality of patient tissue image slides and divide the image slides into patches;
a disease detection and grading module, comprising an artificial intelligence network, wherein the disease detection and grading module is configured to:
receive patient outcome data;
receive a plurality of labels correlated with a disease or lack thereof, wherein each label comprises a morphological type;
covert each patch to a patch vector;
train the artificial intelligence network, based on the plurality of labels to identify the morphological types associated with each label in the image slides;
generate labeled patch vectors, wherein the label of a patch vector is assigned based, at least partly, on the plurality of the labels and the morphological type expressed in the patch;
a morphology detector comprising an unsupervised machine learning model configured to cluster the labeled patch vectors, wherein each cluster corresponds to a morphological subtype expressed in the patch corresponding to the labeled patch vector and a patient outcome; and
a region of interest and outcome prediction module configured to:
receive the clustered labeled vectors and generate a patch-level score for each patch based at least partly on the cluster to which the patch vector of the patch belongs, wherein generating the patch-level score further comprises:
converting regions surrounding each patch to microenvironment vectors;
obtaining mean vectors by averaging the patch vectors corresponding to the patches with the microenvironment vectors;
clustering the mean vectors;
sampling input mean vectors from each cluster; and
using an artificial intelligence model, comprising a plurality of weights, convert each sampled input mean vectors to a patch-level score; and

generate a patient-level score for each patient, at least partly based on the patch-level scores generated for each patient.