Correlative analysis between ocular surface features and carotid plaque : A multimodal machine learning framework

BACKGROUND AND OBJECTIVE The diagnosis of carotid plaques plays an important role in revealing cardiovascular and cerebrovascular diseases, thus attracting widespread research attention. However, most medical examinations rely heavily on specialists and carotid ultrasound images, which are time-consuming, radiative, expensive and limited in tracking disease progression. To alleviate these deficiency, inspired by the human blood supply sequence, a detailed study on the association between carotid plaque and ocular surface image features is proposed in the paper. METHODS This paper systematically verifies the correlation between carotid plaque and ocular surface image through a multi-dimensional feature analysis approach incorporating texture, frequency domain features, and color characteristics. The analysis combines feature selection, confidence evaluation, and distribution property studies to establish robust associations. Besides, multiple machine learning classifiers are used to evaluate the robustness of the extracted features, with subgroup validation conducted across different subsets, systematically assessing the influence of age and gender factors. RESULTS The proposed method achieves high prediction accuracy on 8875 individuals from Hangzhou Wuyunshan Hospital (Hangzhou Institute for Health Promotion), with electronic health record (EHR) features showing the strongest association (Odds Ratios ORs: 4.35 3.90-4.86 in males; 2.92 2.60-3.27 in females). Experimental results demonstrate that age, male gender, and ocular surface image features - including EHR, local binary patterns (LBP), gray-level gradient co-occurrence matrix (GLGCM), and gray-level co-occurrence matrix (GLCM) - show strong associations with carotid plaque, where LBP and EHR features are selected most frequently. CONCLUSIONS Ocular surface image analysis offers a practical and non-invasive method for carotid plaque screening. The observed feature associations and strong predictive performance highlight its potential for clinical applications, especially in large-scale population screening.