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From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning

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机构： [1]Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. [2]Yong Loo Lin School of Medicine, National University of Singapore, Singapore. [3]Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore. [4]Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore. [5]Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China. [6]Department of Ophthalmology, National University Hospital, Singapore. [7]Tsinghua Medicine, Tsinghua University, China.

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Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening.Copyright: © 2023 Soh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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第一作者机构： [1]Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. [2]Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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通讯机构： [1]Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. [2]Yong Loo Lin School of Medicine, National University of Singapore, Singapore. [4]Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.

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