MACHINE LEARNING SYSTEMS AND RELATED ASPECTS FOR THE DETECTION OF DISEASE STATES

• Main Authors: UNBERATH, Mathias, CANARES, Therese, L, SOBERANIS MUKUL, Roger D
• Format: Patent
• Language: English; French; German
• Published: 23.07.2025
• Subjects: DIAGNOSIS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEDICAL OR VETERINARY SCIENCE; PHYSICS; PREPARATIONS FOR MEDICAL, DENTAL, OR TOILET PURPOSES; SURGERY

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.