Abstract
This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length: a “normal group,” a “high myopia group,” and an “other retinal disease” group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range: 97.78–100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.
Highlights
IntroductionStudies have been conducted to diagnose and evaluate the severity of diseases, such as age-related macular degeneration, diabetic retinopathy, and retinopathy of p rematurity[11,12,13,14]
Ocular changes by pathologic myopia can be visualized on OCT images in addition to fundus photographs
This study demonstrated that a deep learning (DL) model using OCT images could distinguish accurately high myopia from normal and other macular diseases
Summary
Studies have been conducted to diagnose and evaluate the severity of diseases, such as age-related macular degeneration, diabetic retinopathy, and retinopathy of p rematurity[11,12,13,14]. Various DL studies using OCT have been conducted especially in age-related macular degeneration but not in high m yopia[18,21,22]. There has been no previous study on whether the DL model can diagnose or screen for high myopia by OCT imaging without measuring the axial length. It will be clinically useful to screen high myopia using OCT image-based DL models. The researchers generated DL models for the screening of high myopia using a pair of horizontal and vertical spectral-domain OCT images. The results of DL classification were compared with classification performed by ophthalmology residents and retinal specialists
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
