Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning

• Published in: Frontiers in medicine Vol. 12; p. 1551557
• Main Authors: Al Younis, Sona M., Ghosh, Samit Kumar, Raja, Hina, Alskafi, Feryal A., Yousefi, Siamak, Khandoker, Ahsan H.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 17.03.2025
• Subjects: cardiovascular diseases; deep learning; explainable AI; heart failure; machine learning; Medicine; UK Biobank; deep learning; heart failure; explainable AI; cardiovascular diseases; machine learning; UK Biobank
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2025.1551557

Over 64 million people worldwide are affected by heart failure (HF), a condition that significantly raises mortality and medical expenses. In this study, we explore the potential of retinal optical coherence tomography (OCT) features as non-invasive biomarkers for the classification of heart failure subtypes: left ventricular heart failure (LVHF), congestive heart failure (CHF), and unspecified heart failure (UHF). By analyzing retinal measurements from the left eye, right eye, and both eyes, we aim to investigate the relationship between ocular indicators and heart failure using machine learning (ML) techniques. We conducted nine classification experiments to compare normal individuals against LVHF, CHF, and UHF patients, using retinal OCT features from each eye separately and in combination. Our analysis revealed that retinal thickness metrics, particularly ISOS-RPE and macular thickness in various regions, were significantly reduced in heart failure patients. Logistic regression, CatBoost, and XGBoost models demonstrated robust performance, with notable accuracy and area under the curve (AUC) scores, especially in classifying CHF and UHF. Feature importance analysis highlighted key retinal parameters, such as inner segment-outer segment to retinal pigment epithelium (ISOS-RPE) and inner nuclear layer to the external limiting membrane (INL-ELM) thickness, as crucial indicators for heart failure detection. The integration of explainable artificial intelligence further enhanced model interpretability, shedding light on the biological mechanisms linking retinal changes to heart failure pathology. Our findings suggest that retinal OCT features, particularly when derived from both eyes, have significant potential as non-invasive tools for early detection and classification of heart failure. These insights may aid in developing wearable, portable diagnostic systems, providing scalable solutions for personalized healthcare, and improving clinical outcomes for heart failure patients.