Ai-based methods and systems for predicting diabetes risk and related metabolic parameters

A system for predicting diabetes in patients, the system comprising:

• a wearable or edge computing device comprising a memory and a processing unit;
• one or more sensors configured to collect physiological parameters including at least one of heart rate, oxygen level, and/or blood pressure;
• a camera module configured to capture silhouette images of the patient;
• a first AI model executable by the processing unit to extract a stress level of the patient based at least on behavioral indicators, historical lifestyle data, and sensor-derived physiological parameters;
• a second AI model executable by the processing unit to extract body mass index (BMI) or fat distribution patterns based at least on silhouette images and weight of the patient;
• and
• a third AI model executable by the processing unit to predict a blood sugar level or diabetes risk score of the patient based on outputs from the first and second AI models and the monitored characteristics.

A non-transitory computer-readable medium having computer-readable instructions that when executed by a processor causes the processor to perform operations of:

• monitoring a plurality of patient-specific characteristics comprising, at least one physiological parameter, one behavioral indicator, and one visual representation of the patient;
• extracting, using a first artificial intelligence (AI) model, a stress level of the patient based at least on behavioral indicators, historical lifestyle data, and sensor-derived physiological parameters;
• extracting, using a second AI model, a body mass index (BMI) or fat distribution patterns based at least on silhouette images and weight of the patient;
• and
• predicting, using a third AI model, a blood sugar level or diabetes risk score of the patient based on outputs from the first and second AI models and the monitored characteristics.