BACKGROUND
Pathologic myopia is a disease that causes vision impairment and blindness. Therefore, it is essential to diagnose it in a timely manner. However, there is no standardized definition for pathologic myopia, and the interpretation of pathologic myopia by optical coherence tomography is subjective and requires considerable time and money. Therefore, there is a need for a diagnostic tool that can diagnose pathologic myopia in patients automatically and in a timely manner.
OBJECTIVE
The purpose of this study was to develop an algorithm that uses optical coherence tomography (OCT) to automatically diagnose patients with pathologic myopia who require treatment.
METHODS
This study was conducted using patient data from patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary's Hospital and Seoul St. Mary's Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. A model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).
RESULTS
Four models developed using test datasets were evaluated and compared. The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC).
CONCLUSIONS
In our study, we developed a deep learning model that can automatically diagnose pathologic myopia without segmentation of 3D optical coherence tomography images. Our deep learning model based on EfficientNetB4 demonstrated excellent performance in identifying pathologic myopia.