# US 2026/0100267 A1 · Systems and Methods for Automated Diagnosis of Disease Related Risk Factors in 3D Biomedical Imaging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100267 A1 - Document ID: US20260100267A1 - Status: Published application - Kind: A1 - Application number: 19/156,782 - Filed: 2024-02-14 - Published: 2026-04-09 - Earliest claimed: 2023-02-14 - Assignees: The Regents of the University of California - Applicants: The Regents of the University of California - Inventors: Oren Avram; Berkin Durmus; Nadav Rakocz; Jeffrey Chiang; Srinivas Sadda; Eran Halperin - Web page: https://patentgrep.com/patent/US20260100267A1 ## CPC - G16H 30/40: for processing medical images, e.g. editing - G06N 3/045: Combinations of networks - G06N 20/00: Machine learning - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Deep learning methods and systems for detecting biomarkers within volumetric biomedical imaging dataset using such deep learning methods and systems are provided. Embodiments predict the clinically useful biomarkers in optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images using deep neural networks. ## Claims ### Claim 1 A method to predict biomarkers in biomedical imaging, comprising: reshaping a plurality of three-dimensional images into a plurality of two-dimensional images by stacking a plurality of slices of said three-dimensional images on top of one another using a computer system; applying a pre-trained feature extractor to the plurality of two-dimensional images, wherein the pre-trained feature extractor independently operates on each of the plurality of two-dimensional images, and generates a plurality of feature maps; applying a convolutional neural network to operate across the plurality of feature maps, wherein the convolutional neural network produces a feature vector; and generating an output of biomarker prediction, wherein the prediction is a transformation of the feature vector; wherein the plurality of three-dimensional images is selected from a group consisting of: optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images. ### Claim 2 (depends on 1) The method of claim 1, wherein the biomarker for optical coherent tomography images is selected from the group consisting of drusen volume (DV), intraretinal hyperreflective foci (IHRF), subretinal drusen deposits (SDD), hyporeflective drusen core (hDC), and any combinations thereof. ### Claim 3 (depends on 1) The method of claim 1, wherein the biomarker for ultrasound images comprises ejection fraction, cardiomyopathy, and a combination thereof. ### Claim 4 (depends on 1) The method of claim 1, wherein the biomarker for magnetic resonance imaging images comprises hepatic proton density fat fraction level. ### Claim 5 (depends on 1) The method of claim 1, wherein the biomarker for computed tomography images comprises nodule malignancy in thoracic cancer. ### Claim 6 (depends on 1) The method of claim 1, further comprising: obtaining a training dataset of images using the computer system; generating a first set of features for each image in the training dataset based upon object classification using the computer system; and training the feature extractor to learn relationships between the set of images in the training dataset and the first set of features in the training dataset using the computer system. ### Claim 7 (depends on 6) The method of claim 6, wherein the training dataset comprises a plurality of ImageNet dataset. ### Claim 8 (depends on 6) The method of claim 6, further comprising: obtaining an annotated training dataset comprising optical coherent tomography images using the computer system, wherein each of the optical coherent tomography images is annotated with at least one retinal disease risk factor; generating a second set of features for each annotated image in the annotated training dataset using the computer system; and training the feature extractor to learn relationships between each annotated image and the second set of features in the annotated training dataset using the computer system. ### Claim 9 (depends on 8) The method of claim 8, wherein the annotated training dataset comprises two-dimensional images of optical coherent tomography images. ### Claim 10 (depends on 9) The method of claim 9, wherein the two-dimensional images are fovea scans. ### Claim 11 (depends on 8) The method of claim 8, wherein the pre-trained feature extractor trained with the annotated training dataset of optical coherent tomography images is used for analyzing three-dimensional images selected from a group consisting of: optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images, and generating biomarker predictions thereof. ### Claim 12 (depends on 1) The method of claim 1, wherein the plurality of slices is stacked linearly to form the two-dimensional image. ### Claim 13 (depends on 1) The method of claim 1, wherein the convolutional neural network comprises a vision transformer module. ### Claim 14 (depends on 1) The method of claim 1, wherein the feature vector transformation is a decision layer comprising at least two fully connected layers. ### Claim 15 A method of training a feature extractor, comprising: obtaining a training dataset of images using a computer system; generating a first set of features for each image in the training dataset based upon object classification using the computer system; and training a feature extractor model to learn relationships between the set of images in the training dataset and the first set of features in the training dataset using the computer system. ### Claim 16 (depends on 15) The method of claim 15, wherein the training dataset comprises a plurality of ImageNet dataset. ### Claim 17 (depends on 15) The method of claim 15, further comprising: obtaining an annotated training dataset of optical coherent tomography images using the computer system, wherein each of the optical coherent tomography images is annotated with at least one retinal disease risk factor; generating a second set of features for each annotated image in the annotated training dataset using the computer system; and training the feature extractor model to learn relationships between each annotated image and the second set of features in the annotated training dataset using the computer system. ### Claim 18 (depends on 17) The method of claim 17, wherein the annotated training dataset comprises two-dimensional images of optical coherent tomography images. ### Claim 19 (depends on 18) The method of claim 18, wherein the two-dimensional images are fovea scans. ### Claim 20 (depends on 17) The method of claim 17, wherein the feature extractor trained with the annotated training dataset of optical coherent tomography images is used for analyzing three-dimensional images selected from a group consisting of: optical coherent tomography images, ultrasound images, magnetic resonance imaging images, and computed tomography images, and generating biomarker predictions thereof. ### Claim 21 (depends on 20) The method of claim 20, wherein the biomarker for optical coherent tomography images is selected from the group consisting of drusen volume (DV), intraretinal hyperreflective foci (IHRF), subretinal drusen deposits (SDD), hyporeflective drusen core (hDC), and any combinations thereof; wherein the biomarker for ultrasound images comprises ejection fraction, cardiomyopathy, and a combination thereof; wherein the biomarker for magnetic resonance imaging images comprises hepatic proton density fat fraction level; wherein the biomarker for computed tomography images comprises nodule malignancy in thoracic cancer.