US 2026/0100282 A1 · MACHINE LEARNING SYSTEMS AND RELATED ASPECTS FOR THE DETECTION OF DISEASE STATES

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.

Claims31

Dependent claims

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.

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.

3 more elements

Dependencies are inferred from the claim text.

Machine learning systems and related aspects for the detection of disease states

Life of this patent

Machine learning systems and related aspects for the detection of disease states

Life of this patent

Abstract

Claims31