This overview examines the integration of machine learning (ML) approaches into diabetes prediction and diagnosis, highlighting the evolution from classical statistical methods to advanced data-driven ...

By Hugo Francisco de Souza A new study shows that gut microbiome signatures, analyzed through advanced machine learning, can help identify individuals with more severe insulin resistance, offering ...

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...

The researchers published their findings in the journal Nature Communications.

A new study suggests that an AI-assisted blood test, which measures for biomarkers, could help to identify prediabetes risk earlier.

RICHMOND, Va. (WRIC) — A research study from the University of Virginia Center for Diabetes Technology suggests that data from continuous glucose monitors (CGMs) can predict the development of serious ...

Time-in-range computed from virtual CGM data predicts retinopathy, neuropathy similarly to glycated hemoglobin data. (HealthDay News) — Fourteen-day continuous glucose monitoring (CGM) traces added to ...