Al Younis Sona M, Ghosh Samit Kumar, Raja Hina, Alskafi Feryal A, Yousefi Siamak, Khandoker Ahsan H
Department of Biomedical Engineering and Biotechnology, Healthcare Engineering Innovation Group (HEIG), Khalifa University, Abu Dhabi, United Arab Emirates.
Department of Mathematics and Computer Science, Fisk University, Nashville, TN, United States.
Front Med (Lausanne). 2025 Mar 17;12:1551557. doi: 10.3389/fmed.2025.1551557. eCollection 2025.
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.
全球有超过6400万人受到心力衰竭（HF）的影响，这种疾病显著提高了死亡率和医疗费用。在本研究中，我们探索视网膜光学相干断层扫描（OCT）特征作为心力衰竭亚型分类的非侵入性生物标志物的潜力，这些亚型包括左心室心力衰竭（LVHF）、充血性心力衰竭（CHF）和未指定的心力衰竭（UHF）。通过分析左眼、右眼和双眼的视网膜测量数据，我们旨在使用机器学习（ML）技术研究眼部指标与心力衰竭之间的关系。我们进行了九项分类实验，将正常个体与LVHF、CHF和UHF患者进行比较，分别使用每只眼睛的视网膜OCT特征以及双眼特征的组合。我们的分析表明，心力衰竭患者的视网膜厚度指标，特别是不同区域的ISOS-RPE和黄斑厚度，显著降低。逻辑回归、CatBoost和XGBoost模型表现出强大的性能，具有显著的准确率和曲线下面积（AUC）得分，尤其是在CHF和UHF的分类中。特征重要性分析突出了关键的视网膜参数，如内节-外节至视网膜色素上皮（ISOS-RPE）以及内核层至外界膜（INL-ELM）的厚度，作为心力衰竭检测的关键指标。可解释人工智能的整合进一步增强了模型的可解释性，揭示了将视网膜变化与心力衰竭病理联系起来的生物学机制。我们的研究结果表明，视网膜OCT特征，特别是来自双眼的特征，作为心力衰竭早期检测和分类的非侵入性工具具有巨大潜力。这些见解可能有助于开发可穿戴、便携式诊断系统，为个性化医疗提供可扩展的解决方案，并改善心力衰竭患者的临床结局。
