Radiomics-Based Fundus Photography Analysis in Diabetic Retinopathy

The purpose of this study was to apply radiomics feature extraction from fundus photographs to automatically identify biomarkers that distinguish different stages of diabetic retinopathy (DR).

A total of 52 radiomic features were extracted from fundus photographs representing different DR stages: diabetes without DR (n = 35), mild non-proliferative DR (NPDR; n = 33), moderate NPDR (n = 35), severe NPDR (n = 35), and proliferative DR (PDR; n = 34). Additionally, 68 images from 34 eyes with NPDR over 1 year of follow-up were analyzed. Statistical significance was evaluated using 95% bootstrap confidence intervals. Wilcoxon rank-sum tests, linear regression, and multi-class logistic regression were performed using R software (version 4.5.1) and Python software.

When comparing eyes without DR to all NPDR eyes, significant differences were detected in 6 of 16 first-order statistics (FOS), 1 of 14 gray-level co-occurrence matrix (GLCM), 13 of 16 Gabor texture (GT), and 1 of 6 Law's texture energy (LTE) features (P < 0.01). Several features demonstrated a linear trend with increasing DR severity, whereas PDR showed a distinct pattern. Across all 5 stages, 14 FOS, 7 GLCM, 11 GT, and 1 LTE features differed significantly (P < 0.05). Overall, 34 of 52 features significantly distinguished no DR from NPDR, and 17 discriminated across all stages. Longitudinally, 34 NPDR eyes showed no significant 1-year changes.

Radiomic features from fundus photographs may help in distinguishing eyes without DR from NPDR, demonstrating strong potential for automated DR classification and screening applications.

Radiomic-derived biomarkers from fundus photographs may provide automated, objective support for DR screening and staging.