Retina (Philadelphia, Pa.) · 2026
Identification and localization of myopic macular neovascularization using deep learning.
СтатьяНазвание и аннотация: Перевод в очереди
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Moalem Hanna, Shlezinger Einav Baharav, Tiosano Alon, Fradkin Noam, Yacobi Bar, Chacham Shannie, Keshet Yariv, Rabina Gilad, Dotan Assaf, Bahar Irit, Gal-Or Orly
Аннотация
PURPOSE: Myopia is rapidly increasing worldwide, with macular neovascularization (MNV) representing a major cause of vision loss. Early diagnosis remains challenging. Our study aims to incorporate a deep learning (DL) model to identify and localize myopic MNV using OCT images.
METHODS: A total of 6,215 OCT B-scan images of patients with pathologic myopia were included, of which 1,664 contained MNV. MNV lesions were manually annotated and validated by retina specialists. The dataset included additional myopia-related pathologies to account for factors affecting model robustness. Data were split into training and validation sets using patient-level 5-fold cross-validation. A YOLOv11 (You Only Look Once) object detection model was trained to localize MNV. Two postprocessing pipelines using either non-maximum suppression (NMS) or weighted box fusion (WBF) were tested. Model performance was evaluated at 25% intersection over union (IoU) using precision, recall, and mean average precision (mAP25).
RESULTS: The YOLOv11 models achieved mean precision ranging from 0.73 to 0.79 and recall from 0.76 to 0.80. The highest mAP25 reached 0.84. Adapting the model's output into an image-level binary classification yielded precision up to 0.85, recall up to 0.88, and F1-score up to 0.85.
CONCLUSION: A YOLOv11-based deep learning model reliably detects and localizes myopic MNV on OCT imaging, overcoming the challenge of structurally complex eyes with coexisting myopic pathologies. This tool may serve as an automated reader to assist with earlier diagnosis and timely anti-VEGF intervention in patients with pathologic myopia.
METHODS: A total of 6,215 OCT B-scan images of patients with pathologic myopia were included, of which 1,664 contained MNV. MNV lesions were manually annotated and validated by retina specialists. The dataset included additional myopia-related pathologies to account for factors affecting model robustness. Data were split into training and validation sets using patient-level 5-fold cross-validation. A YOLOv11 (You Only Look Once) object detection model was trained to localize MNV. Two postprocessing pipelines using either non-maximum suppression (NMS) or weighted box fusion (WBF) were tested. Model performance was evaluated at 25% intersection over union (IoU) using precision, recall, and mean average precision (mAP25).
RESULTS: The YOLOv11 models achieved mean precision ranging from 0.73 to 0.79 and recall from 0.76 to 0.80. The highest mAP25 reached 0.84. Adapting the model's output into an image-level binary classification yielded precision up to 0.85, recall up to 0.88, and F1-score up to 0.85.
CONCLUSION: A YOLOv11-based deep learning model reliably detects and localizes myopic MNV on OCT imaging, overcoming the challenge of structurally complex eyes with coexisting myopic pathologies. This tool may serve as an automated reader to assist with earlier diagnosis and timely anti-VEGF intervention in patients with pathologic myopia.