A Deep Learning Approach for Classification of the Primary Angle-closure Disease Spectrum Based on Anterior Segment Optical Coherence Tomography.

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Abstract

We developed a deep learning-based classifier that can discriminate primary angle-closure suspects, primary angle-closure/primary angle-closure glaucoma, and also control eyes with open-angle with acceptable accuracy.To develop a deep learning (DL) based classifier for differentiating subtypes of primary angle closure disease (PACD), including primary angle-closure suspect (PACS) and primary angle-closure/primary angle-closure glaucoma (PAC/PACG) and also normal control eyes.Anterior segment optical coherence tomography (AS-OCT) images were used for analysis with five different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each above-mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction.A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean±SD age was 51.76±15.15 years and 48.3% were male. MobileNet had the best performance in the model in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99±0.00, 0.77±0.02, and 0.77±0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95±0.03, 0.83±0.06, and 0.81±0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test dataset.The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on AS-OCT images.Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.