# US 2026/0100282 A1 · Machine learning systems and related aspects for the detection of disease states > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100282 A1 - Document ID: US20260100282A1 - Status: Published application - Kind: A1 - Application number: 19/112,143 - Filed: 2023-09-14 - Published: 2026-04-09 - Earliest claimed: 2022-09-16 - Assignees: The Johns Hopkins University - Applicants: The Johns Hopkins University - Inventors: Therese L. CANARES; Mathias UNBERATH; Roger D. SOBERANIS MUKUL - Web page: https://patentgrep.com/patent/US20260100282A1 ## CPC - 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 30/40: for processing medical images, e.g. editing - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Examples may provide an electronic neural network (ENN) that has been trained on a set of training data that comprises sets of features extracted from oral cavity-related data obtained from reference subjects. The oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject. Predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the ENN is trained. The ENN outputs a prediction score for the disease state in a test subject that is indicated by a set of features extracted from oral cavity-related data obtained from the test subject when the set of features is passed through the ENN. ## Claims ### Claim 1 A computer-implemented method of generating a prediction score for a disease state in a test subject, the method comprising: passing a first set of features extracted from oral cavity-related data obtained from a test subject through an electronic neural network, wherein the electronic neural network has been trained on a first set of training data that comprises a plurality of sets of features extracted from oral cavity-related data obtained from reference subjects, wherein the oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject, and wherein one or more predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the electronic neural network is trained; and, outputting from the electronic neural network the prediction score for the disease state in the test subject indicated by the first set of features extracted from the oral cavity-related data from the test subject. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, comprising generating a therapy recommendation for the test subject based upon the prediction score output from the electronic neural network. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, comprising administering a therapy to the test subject based upon the prediction score output from the electronic neural network. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the oral cavity-related data comprises oral cavity images. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the oral cavity-related data comprises image data, demographic data, symptom data, physical examination data, or a combination thereof. ### Claim at position 6 -8. (canceled) ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the disease state comprises a bacterial infection, a viral infection, or a peritonsillar abscess. ### Claim at position 8 -12. (canceled) ### Claim 13 (depends on 1) The computer-implemented method of claim 1, wherein the prediction score comprises a probability of a positive or negative streptococcus pharyngitis classification for the test subject. ### Claim 14 (depends on 1) The computer-implemented method of claim 1, wherein the oral cavity-related data comprises oral cavity images from the test and reference subjects, which oral cavity images comprise a region of interest selected from the group consisting of: a throat area, a tonsil area, a tongue area, a palate area, uvula area, posterior oropharynx area, lips area, cheek area, and neck area. ### Claim at position 11 -20. (canceled) ### Claim 21 (depends on 1) The computer-implemented method of claim 1, wherein the oral cavity data from the test and reference subjects are obtained from videos of the test and reference subjects. ### Claim at position 13 -25. (canceled) ### Claim 26 (depends on 1) The computer-implemented method of claim 1, wherein the first set of training data comprises oral cavity images and wherein the electronic neural network has been further trained on a second set of training data that comprises a plurality of sets of features extracted from numerical vectors representing sets of parameterized demographic data, symptom data, and/or physical examination data from the reference subjects and wherein the computer-implemented method further comprises passing a second set of features extracted from a numerical vector representing a set of parameterized demographic data, symptom data, and/or physical examination data from the test subject through the electronic neural network. ### Claim 27 (depends on 26) The computer-implemented method of claim 26, wherein the numerical vectors representing the set of parameterized demographic data, symptom data, and/or physical examination data from the reference subjects and from the test subject each comprise at least a 15-dimensional vector. ### Claim 28 (depends on 26) The computer-implemented method of claim 26, further comprising mapping the first and second sets of features to a bidimensional vector that corresponds to the prediction score for the disease state in the test subject. ### Claim at position 17 -54. (canceled) ### Claim 55 A system for generating a prediction score for a disease state in a test subject using an electronic neural network, the system comprising: a processor; and a memory communicatively coupled to the processor, the memory storing instructions which, when executed on the processor, perform operations comprising: passing a first set of features extracted from oral cavity-related data obtained from a test subject through an electronic neural network, wherein the electronic neural network has been trained on a first set of training data that comprises a plurality of sets of features extracted from oral cavity-related data obtained from reference subjects, wherein the oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject, and wherein one or more predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the electronic neural network is trained; and outputting from the electronic neural network a prediction score for the disease state in the test subject indicated by the first set of features extracted from the oral cavity-related data from the test subject. ### Claim 56 (depends on 55) The system of claim 55, wherein the instructions which, when executed on the processor, further perform operations comprising: generating a therapy recommendation for the test subject based upon the prediction score output from the electronic neural network. ### Claim 57 (canceled) ### Claim 58 (depends on 55) The system of claim 55, wherein the oral cavity-related data comprises image data, demographic data, symptom data, physical examination data, or a combination thereof. ### Claim at position 22 -61. (canceled) ### Claim 62 (depends on 55) The system of claim 55, wherein the disease state comprises a bacterial infection, a viral infection, or a peritonsillar abscess. ### Claim at position 24 -65. (canceled) ### Claim 66 (depends on 55) The system of claim 55, wherein the prediction score comprises a probability of a positive or negative streptococcus pharyngitis classification for the test subject. ### Claim 67 (depends on 55) The system of claim 55, wherein the oral cavity-related data comprises oral cavity images from the test and reference subjects, which oral cavity images comprise a region of interest selected from the group consisting of: a throat area, a tonsil area, a tongue area, a palate area, uvula area, posterior oropharynx area, lips area, cheek area, and neck area. ### Claim at position 27 -75. (canceled) ### Claim 76 (depends on 55) The system of claim 55, wherein the first set of training data comprises oral cavity images and wherein the electronic neural network has been further trained on a second set of training data that comprises a plurality of sets of features extracted from numerical vectors representing sets of parameterized demographic data, symptom data, and/or physical examination data from the reference subjects and wherein the computer-implemented method further comprises passing a second set of features extracted from a numerical vector representing a set of parameterized demographic data, symptom data, and/or physical examination data from the test subject through the electronic neural network. ### Claim at position 29 -100. (canceled) ### Claim 101 A computer readable media comprising non-transitory computer executable instructions which, when executed by at least one electronic processor, perform at least: passing a first set of features extracted from oral cavity-related data obtained from a test subject through an electronic neural network, wherein the electronic neural network has been trained on a first set of training data that comprises a plurality of sets of features extracted from oral cavity-related data obtained from reference subjects, wherein the oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject, and wherein one or more predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the electronic neural network is trained; and outputting from the electronic neural network a prediction score for the disease state in the test subject indicated by the first set of features extracted from the oral cavity-related data from the test subject. ### Claim 102 (canceled)