Automated Nonperfusion Quantification in Diabetic Retinopathy on Ultra-Widefield Swept-Source OCT Angiography

Purpose: To evaluate the performance of a customized deep learning algorithm for automated segmentation of nonperfusion area (NPA) on ultra-widefield swept-source OCTA (UWF SS-OCTA) and its utility in diabetic retinopathy (DR) severity assessment. Design: Cross-sectional study. Subjects: A total of 180 eyes from 122 participants representing all grades of DR severity. Methods: We developed a convolutional neural network based on a multiscale U-Net backbone with squeeze-and-excitation attention for segmentation of NPAs on en face SS-OCTA all-retinal-layer images from 3 scan patterns: 6 × 6 mm, 12 × 12 mm, and 29 × 24 mm. Ground-truth annotations of NPAs and nongradable area (NGA) on en face OCTA images were generated by 2 independent graders and adjudicated by a vitreoretinal specialist. A corresponding en face structural OCT image was incorporated to distinguish true NPAs from shadow artifacts. Segmentation outputs included NPA, NGA, and shadow artifacts. Pixel-level accuracy was assessed with the F1 score. Nonperfusion index (NPI) was defined as NPA/gradable area. The level of agreement between human-labeled and algorithm-predicted NPI was analyzed using Bland-Altman analysis. Main Outcome Measures: Algorithm F1 score and NPI. Results: < 0.001) with the largest magnitude of increase in 29 × 24 mm scans. The algorithm for foveal avascular zone segmentation also achieved a mean F1 score of 0.88 ± 0.05 for 6 × 6 mm images and 0.85 ± 0.05 for 12 × 12 mm images. Conclusions: This deep learning algorithm was validated on single-scan UWF SS-OCTA for automated NPA segmentation and quantification. It demonstrates high accuracy and scalability across multiple scan sizes, supporting its potential integration into objective DR OCTA biomarker analysis. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.