Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomic Features from Multimodal Retinal Images.

ABSTRACT:

Purpose

To develop a machine learning (ML) algorithm capable of determining cardiovascular (CV) risk in multimodal retinal images from patients with type 1 diabetes mellitus (T1DM), distinguishing between moderate, high, and very high-risk levels.

Design

Cross-sectional analysis of a retinal image data set from a previous prospective OCT angiography (OCTA) study (ClinicalTrials.gov NCT03422965).

Participants

Patients with T1DM included in the progenitor study.

Methods

Radiomic features were extracted from color fundus photographs (CFPs), OCT, and OCTA images, and ML models were trained using these features either individually or combined with clinical data (demographics and systemic data, OCT + OCTA commercial software metrics, ocular data, blood data). Differe

SUBMITTER: Toha-Dalmau A 

PROVIDER: S-EPMC12391792 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature