<ul><li>This study aimed to develop a machine learning (ML) algorithm for determining cardiovascular risk in type 1 diabetes mellitus patients using multimodal retinal images.</li><li>Radiomic features from fundus retinography, optical coherence tomography (OCT), and OCT angiography (OCTA) images were extracted.</li><li>ML models trained with radiomic features achieved AUC values of 0.79 for identifying moderate risk cases from high and very high-risk cases, and 0.73 for distinguishing between high and very high-risk cases.</li><li>The addition of clinical variables improved AUC values, reaching 0.99 for identifying moderate risk cases and 0.95 for differentiating between high and very high-risk cases.</li></ul>

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

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