Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
It addresses cardiovascular risk assessment for patients with type 1 diabetes mellitus, offering a non-invasive oculomics approach, but it is incremental as it applies existing radiomics and ML methods to a new medical application.
This study developed a machine learning algorithm to predict cardiovascular risk levels (moderate, high, very high) in patients with type 1 diabetes mellitus using radiomic features from multimodal retinal images, achieving AUC values up to 0.99 when combined with clinical data.
This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients with type 1 diabetes mellitus, distinguishing between moderate, high, and very high-risk levels. Radiomic features were extracted from fundus retinography, optical coherence tomography (OCT), and OCT angiography (OCTA) images. ML models were trained using these features either individually or combined with clinical data. A dataset of 597 eyes (359 individuals) was analyzed, and models trained only with radiomic features achieved AUC values of (0.79 $\pm$ 0.03) for identifying moderate risk cases from high and very high-risk cases, and (0.73 $\pm$ 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, reaching (0.99 $\pm$ 0.01) for identifying moderate risk cases and (0.95 $\pm$ 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT+OCTA metrics and ocular data achieved an AUC of (0.89 $\pm$ 0.02) without systemic data input. These results demonstrate that radiomic features obtained from multimodal retinal images are useful for discriminating and classifying CV risk labels, highlighting the potential of this oculomics approach for CV risk assessment.