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Deep Learning Assessment of Intraretinal Microvascular Abnormalities in Diabetic Retinopathy on Swept-Source OCT
Pei Qi Lo1,2, Ser Koon Goh1,2, Wenjun Song1
1Department of Ophthalmology, Tan Tock Seng Hospital, National Health Group Eye Institute, Singapore, Singapore.
Purpose:
To develop and evaluate a deep learning algorithm for automated segmentation and quantification of intraretinal microvascular abnormalities (IRMAs) on swept-source OCT angiography (SS-OCTA) across multiple fields of view (FOVs) and explore IRMA burden as an OCT angiography (OCTA) biomarker for stratifying diabetic retinopathy (DR) severity and correlation with retinal nonperfusion.
Design:
Cross-sectional study.
Subjects:
A total of 254 eyes from 181 patients with varying stages of DR, including nonproliferative DR (NPDR) and proliferative DR (PDR).
Methods:
Eyes of different DR stages were imaged using 6 × 6 mm, 12 × 12 mm, and 29 × 24 mm scans. A context-enhanced U-Net (CE-U-Net) incorporating nonperfusion area priors and attention-based feature recalibration was trained to segment IRMAs on en face all-retinal-layer OCTA images.
Main Outcome Measures:
Performance was evaluated using lesion-level F1 score, intraclass correlation coefficients (ICCs) for quantitative agreement with ground-truth IRMA counts, and Bland-Altman analysis. Intraretinal microvascular abnormality counts were compared across DR severity stages and correlated with nonperfusion index (NPI).
Results:
A total of 254 OCTA scans were analyzed (NPDR = 60.2%, PDR = 39.8%). The CE-U-Net achieved mean F1 scores of 0.61 ± 0.08, 0.71 ± 0.02, and 0.65 ± 0.05 for IRMA segmentation on 6 × 6 mm, 12 × 12 mm, and 29 × 24 mm scans, respectively. Algorithm-predicted IRMA counts showed strong agreement with ground-truth across all FOVs (ICC = 0.81, 0.86, and 0.82, respectively). OCT angiography-detectable IRMAs were present from mild-to-moderate NPDR and increased markedly from mild-to-moderate NPDR to severe NPDR, particularly in 29 × 24 mm scans (P < 0.001), before plateauing in PDR (P = 0.68). The strongest correlation between IRMA counts and NPI was observed in 12 × 12 mm scans across all DR stages (P = 0.01, <0.001, and <0.001, respectively).
Conclusions:
Automated IRMA segmentation and quantification on SS-OCTA is feasible and reproducible across multiple FOVs, including ultra-widefield scans. By reliably capturing both clinically visible and OCTA-detectable "occult" IRMAs, this approach supports IRMA burden as a quantitative biomarker for stratifying DR severity and advances the case for biomarker-driven DR assessment.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
