Title of Paper:Automatic measurement of anterior chamber angle parameters in AS-OCT images using deep learning

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Affiliation of Author(s):[1]Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Sichuan, Peoples R China;[2]Shanxi Med Univ, Shanxi Eye Hosp Affiliated, Dept Cataract, Taiyuan 030001, Shanxi, Peoples R China;[3]Univ Elect Sci & Technol China, Sch Med, Chengdu 610054, Sichuan, Peoples R China

Journal:BIOMEDICAL OPTICS EXPRESS

Key Words:Convolutional neural networks - Deep learning - Image analysis - Learning systems - Medical imaging - Ophthalmology - Optical tomography - Parameter estimation - Risk assessment

Abstract:The early assessment of angle closure is of great significance for the timely diagnosis and treatment of primary angle-closure glaucoma (PACG). Anterior segment optical coherence tomography (AS-OCT) provides a fast and non-contact way to evaluate the angle close using the iris root (IR) and scleral spur (SS) information. The objective of this study was to develop a deep learning method to automatically detect IR and SS in AS-OCT for measuring anterior chamber (AC) angle parameters including angle opening distance (AOD), trabecular iris space area (TISA), trabecular iris angle (TIA), and anterior chamber angle (ACA). 3305 AS-OCT images from 362 eyes and 203 patients were collected and analyzed. Based on the recently proposed transformer-based architecture that learns to capture long-range dependencies by leveraging the self-attention mechanism, a hybrid convolutional neural network (CNN) and transformer model to encode both local and global features was developed to automatically detect IR and SS in AS-OCT images. Experiments demonstrated that our algorithm achieved a significantly better performance than state-of-the-art methods for AS-OCT and medical image analysis with a precision of 0.941, a sensitivity of 0.914, an F1 score of 0.927, and a mean absolute error (MAE) of 37.1 +/- 25.3 mu m for IR, and a precision of 0.805, a sensitivity of 0.847, an F1 score of 0.826, and an MAE of 41.4 +/- 29.4 mu m for SS, and a high agreement with expert human analysts for AC angle parameter measurement. We further demonstrated the application of the proposed method to evaluate the effect of cataract surgery with IOL implantation in a PACG patient and to assess the outcome of ICL implantation in a patient with high myopia with a potential risk of developing PACG. The proposed method can accurately detect IR and SS in AS-OCT images and effectively facilitate the AC angle parameter measurement for pre- and post-operative management of PACG. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Document Type:Article

Volume:14

Issue:4

Page Number:1378-1392

ISSN No.:2156-7085

Translation or Not:no