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

• URL: http://arxiv.org/abs/2504.02868v1
• Date: Tue, 01 Apr 2025 10:25:38 GMT
• Title: Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
• Authors: Ariadna Tohà-Dalmau, Josep Rosinés-Fonoll, Enrique Romero, Ferran Mazzanti, Ruben Martin-Pinardel, Sonia Marias-Perez, Carolina Bernal-Morales, Rafael Castro-Dominguez, Andrea Mendez, Emilio Ortega, Irene Vinagre, Marga Gimenez, Alfredo Vellido, Javier Zarranz-Ventura,
• Abstract summary: Radiomic features were extracted from fundus retinography, optical coherence tomography, and OCT angiography images.<n>Radiomics combined with OCT+ OCTA metrics and ocular data achieved an AUC of (0.89 $pm$ 0.02) without systemic data input.<n>These results demonstrate that radiomic features obtained from multimodal retinal images are useful for discriminating and classifying CV risk labels.
• Score: 0.050721462368721396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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