Translational vision science & technology · 2026
Radiomics-Based Fundus Photography Analysis in Diabetic Retinopathy.
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Sadeghi Elham, Williamson Ryan Chace, Corona Francesca, Davis Elli, Kozlov Michael, Vupparaboina Kiran Kumar, Bollepalli Sandeep Chandra, Sahel José-Alain, Chhablani Jay, Waxman Evan L
Аннотация
PURPOSE: 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).
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Radiomic features from fundus photographs may help in distinguishing eyes without DR from NPDR, demonstrating strong potential for automated DR classification and screening applications.
TRANSLATIONAL RELEVANCE: Radiomic-derived biomarkers from fundus photographs may provide automated, objective support for DR screening and staging.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Radiomic features from fundus photographs may help in distinguishing eyes without DR from NPDR, demonstrating strong potential for automated DR classification and screening applications.
TRANSLATIONAL RELEVANCE: Radiomic-derived biomarkers from fundus photographs may provide automated, objective support for DR screening and staging.