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Abstract #92001 Published in IGR 22-1
Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Hashimoto Y; Asaoka R; Kiwaki T; Sugiura H; Asano S; Murata H; Fujino Y; Matsuura M; Miki A; Mori K; Ikeda Y; Kanamoto T; Yamagami J; Inoue K; Tanito M; Yamanishi KBritish Journal of Ophthalmology 2021; 105: 507-513
BACKGROUND/AIM: To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT). METHODS: This multicentre, cross-sectional study included paired Humphrey field analyser (HFA) 10-2 VF and SD-OCT measurements from 591 eyes of 347 patients with open-angle glaucoma (OAG) or normal subjects for the training data set. We trained a convolutional neural network (CNN) for predicting VF threshold (TH) sensitivity values from the thickness of the three macular layers: retinal nerve fibre layer, ganglion cell layer+inner plexiform layer and outer segment+retinal pigment epithelium. We implemented pattern-based regularisation on top of CNN to avoid overfitting. Using an external testing data set of 160 eyes of 131 patients with OAG, the prediction performance (absolute error (AE) and R between predicted and actual TH values) was calculated for (1) mean TH in whole VF and (2) each TH of 68 points. For comparison, we trained support vector machine (SVM) and multiple linear regression (MLR). RESULTS: AE of whole VF with CNN was 2.84±2.98 (mean±SD) dB, significantly smaller than those with SVM (5.65±5.12 dB) and MLR (6.96±5.38 dB) (all, p<0.001). Mean of point-wise mean AE with CNN was 5.47±3.05 dB, significantly smaller than those with SVM (7.96±4.63 dB) and MLR (11.71±4.15 dB) (all, p<0.001). R with CNN was 0.74 for the mean TH of whole VF, and 0.44±0.24 for the overall 68 points. CONCLUSION: DL model showed considerably accurate prediction of HFA 10-2 VF from SD-OCT.
Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
Full articleClassification:
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.30 Other (Part of: 6 Clinical examination methods)
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)