In:
British Journal of Ophthalmology, BMJ, Vol. 105, No. 4 ( 2021-04), p. 507-513
Abstract:
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 2 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 2 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.
Type of Medium:
Online Resource
ISSN:
0007-1161
,
1468-2079
DOI:
10.1136/bjophthalmol-2019-315600
Language:
English
Publisher:
BMJ
Publication Date:
2021
detail.hit.zdb_id:
1482974-5
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