Abstract
PURPOSE: To develop a deep learning (DL) model for screening posterior staphylomas in highly myopic patients using ultra-widefield optical coherence tomography (UWF-OCT) images. METHODS: Our retrospective single-center study collected 1428 qualified UWF-OCT images from 438 highly myopic patients between 2017 and 2019 for model development. An independent test dataset for internal validation included 216 images from 69 highly myopic patients obtained between June 2020 and December 2020. Posterior staphylomas were detected by identifying the staphyloma edges. Seven independent architectures (VGG16, VGG19, ResNet18, ResNet50, ResNet101, DenseNet121, and DenseNet161) were used to train the models and identify staphyloma edges. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate and compare the performance of each model. RESULTS: The AUCs of seven DL models ranged from 0.794 (95% confidence interval [CI], 0.708-0.875) to 0.903 (95% CI, 0.846-0.953) for staphyloma edge detection in the internal test dataset. VGG19, with the highest AUC, achieved sensitivity (0.871; 95% CI, 0.773-0.931) that was comparable to or better than those of retina specialists. Heatmaps showed that the DL models could precisely identify the region of staphyloma edges. CONCLUSIONS: Our models reliably identified staphyloma edges with high sensitivity and specificity. Given that posterior staphylomas are a key contributor to various fundus complications, the development of DL models holds significant promise for improving the clinical management of highly myopic patients. TRANSLATIONAL RELEVANCE: This effective artificial intelligence system can help ophthalmologists screen posterior staphylomas in highly myopic eyes.