An Automated Diagnosis of Myopia from an Optic Disc Image Using YOLOv11: A Feasible Approach for Non-Expert ECPs in Computer Vision

Myopia is a common refractive error with a rising prevalence worldwide, and its early diagnosis is crucial to prevent long-term visual impairment. This study presents an accessible, automated approach for detecting myopia from fundus photographs by analyzing the optic disc, using a deep learning model based on the YOLO (You Only Look Once) architecture, version 8 and 11. The pipeline was designed to be usable by eye care practitioners (ECPs) with no expertise in computer science. Fundus images were processed to extract the optic disc region using a custom-trained YOLOv8 model, and a subsequent classification algorithm determined the presence or absence of myopia based on features from the extracted region. The system was trained on a single-clinic dataset of 730 augmented images, with 98 images reserved for internal validation, and tested on 50 independent optic disc images. It achieved a high diagnostic accuracy, with strong sensitivity and F1 scores. Lightweight models such as YOLOv11-nano performed comparably to larger variants in the testing dataset (AUC 97.5% vs. 97.3%), effectively supporting myopia detection. This work highlights the feasibility of integrating AI-based screening tools into clinical practice without requiring advanced technical skills, offering a scalable and cost-effective solution to support early diagnosis of myopia in diverse healthcare settings.