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.
Yan et al. (Thu,) studied this question.