Abstract
<jats:p>Purpose: To evaluate the ability of computer vision models to detect myopia from standard colour fundus photographs and to compare their diagnostic performance with that of experienced ophthalmologists. Methods: A total of 324 retinal fundus images were labelled as myopic or non-myopic using cycloplegic subjective refraction. Images were acquired with a non-mydriatic 45° fundus camera and split into training, internal validation (80 images), and independent testing sets (50 images). YOLOv8 and YOLOv11 variants were trained for binary classification. Performance was evaluated using standard diagnostic metrics, with bootstrap confidence intervals and Holm–Bonferroni correction. Five ophthalmologists independently classified the test set, and their consensus was compared with model predictions using DeLong and McNemar tests. Results: On internal validation, YOLOv8-xl achieved the highest sensitivity (0.983) and MCC (0.646). YOLOv11 models showed greater variability, with the extra-large variant displaying degenerate behaviour. On the independent test set, YOLOv8-n achieved an AUC of 0.860 versus 0.753 for YOLOv11-s, with no significant difference (p = 0.174). Clinical consensus reached an AUC of 0.832 and did not differ from YOLOv8-n (p = 0.725). McNemar testing showed no difference between YOLOv8-n and clinicians (p = 1.000), while YOLOv11-s differed significantly (p &amp;lt; 0.001). Limitations include the small, class-imbalanced dataset and the limited number of ophthalmologists. Conclusions: YOLOv8 models can detect myopia from standard fundus photographs with performance comparable to experienced ophthalmologists, supporting their potential as complementary screening tools.</jats:p>