Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning.

Purpose: The purpose of this study was to develop a deep learning algorithm capable of accurately classifying diabetic macular edema (DME) subtypes and segmenting the lesions in patients with diabetic retinopathy (DR) using structural optical coherence tomography (OCT) images. Methods: We retrospectively collected 3120 DME OCT B-scan images from 823 eyes of patients with DME, acquired from 4 different spectral-domain and swept-source OCT devices (Topcon 3D-2000, Topcon Triton, BK400K UWF SS-OCT, and VG200 SS-OCT) to enhance device diversity and evaluate cross-device generalizability. An annotation team, consisting of two mid-career ophthalmologists and one senior retinal specialist, performed meticulous multi-label annotations, including DME subtype categories, detection bounding boxes, and pixel-level segmentation masks, to build the DME-Seg dataset. Based on this dataset, we fine-tuned the YOLO11x-Seg model for the detection and segmentation tasks. Results: The fine-tuned model achieved promising performance on the DME-Seg dataset. For lesion detection, it attained an average mAP50(B) of 0.82, mAP50-95(B) of 0.56, and Dice coefficients of 0.82 ± 0.20 (95% confidence interval [CI] = 0.81-0.83). For segmentation, it achieved an mAP50(M) of 0.84, mAP50-95(M) of 0.54, and Dice coefficients of 0.79 ± 0.18 (95% CI = 0.78-0.80). Conclusions: The constructed DME-Seg dataset and the validated model demonstrate promising performance in the automated detection and segmentation of DME subtypes, with encouraging cross-device generalization capability. This resource provides a foundation for advancing artificial intelligence (AI)-assisted diagnosis and personalized treatment planning for DME. Translational Relevance: This automated quantification tool bridges the gap between AI research and clinical utility by assisting ophthalmologists in the precise diagnosis and treatment of DME.