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Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography Publisher Pubmed



Eslami Y1 ; Mousavi Kouzahkanan Z3 ; Farzinvash Z1 ; Safizadeh M1 ; Zarei R1 ; Fakhraie G1 ; Vahedian Z1 ; Mahmoudi T4 ; Fadakar K1 ; Beikmarzehei A2 ; Tabatabaei SM1
Authors
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Authors Affiliations
  1. 1. Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Medical School, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. School of ECE, College of Engineering, University of Tehran, Tehran, Iran
  4. 4. Department of Medical Physics and Biomedical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Source: Journal of Glaucoma Published:2023


Abstract

Precis: We developed a deep learning-based classifier that can discriminate primary angle closure suspects , primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. Purpose: To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. Materials and Methods: Anterior segment optical coherence tomography images were used for analysis with 5 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 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. Results: 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 males. 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 data set. Conclusion: The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images. © 2023 Lippincott Williams and Wilkins. All rights reserved.
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