Retinal Tortuosity Biomarkers as Early Indicators of Disease: Validation of a Comprehensive Analytical Framework

Retinal blood vessel tortuosity is a promising early biomarker for diseases such as diabetic retinopathy. However, the lack of a standardized evaluation method hinders its clinical application. This study presents a framework with 50 features, including 32 developed and refined from our prior unpublished work. All features were tested for sensitivity and scaling to ensure robust performance. To address the influence of blood vessels’ representation on tortuosity estimation, we tested several resampling approaches and proposed the 1-Equidistant Pixel Sampling method (1EPS), which demonstrated accuracy and approximate scale invariance. The framework was evaluated on a public retinal tortuosity dataset, RET-TORT, consisting of 30 arteries and 30 veins ranked in increased tortuosity. Data augmentation expanded the dataset to 330 arteries and veins for improved reliability. Spearman’s rank correlation coefficient analysis revealed resampling variations in tortuosity estimation, with our method outperforming literature and most features favoring arteries. Using the augmented dataset, Gaussian Process Regression achieved near-perfect performance (R2 = 1.0 for arteries; 0.999 for veins). Feature selection analysis identified artery- and vein-specific features. This work highlights the importance of accurate vessel preprocessing and feature sensitivity to scaling on tortuosity estimation and introduces a scalable, robust framework of 50 hand-crafted features for clinical tortuosity assessment.