Ophthalmology science · 2026
Deep Learning Assessment of Intraretinal Microvascular Abnormalities in Diabetic Retinopathy on Swept-Source OCT Angiography.
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Lo Pei Qi, Goh Ser Koon, Song Wenjun, Goh Ming Hui Colin, Yu Zhongqing, Tan Ryan Zi Lin, Paniharam Pavan, Shen Xiaofeng, Yang Jianxie, Tan Bingyao, Qiu Yue, Loh Tai Yong, Sia Juling, Seah Wei Hing, Zhuang Lingyi, Ang Charmayne Xinyi, Asad Yusra, Bashajan Sowkath Ali, Yong Ying Jie, Mah Jia Jia, Wang Min, Agrawal Rupesh, Zhou Sandy Wenting
Аннотация
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.
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.