Estimating global visual field indices in glaucoma by combining macula and optic disc OCT scans using 3D convolutional neural networks

Purpose To evaluate the accuracy at which visual field global indices could be estimated from Optical Coherence Tomography (OCT) scans of the retina using deep neural networks, and to quantify the contributions to the estimates by the macula (MAC) and the optic nerve head (ONH). Design Observational cohort study. Participants 10,370 eyes from 109 healthy, 697 glaucoma suspect, and 872 glaucoma patients (10,370 in total) over multiple visits (median=3). Methods 3D convolutional neural networks were trained to estimate global visual field indices derived from automated Humphrey perimetry (SITA 24-2) tests, using OCT scans centered on MAC, ONH, or both (MAC+ONH) as inputs. Main Outcome Measures Spearman's rank correlation coefficients, Pearson's correlation coefficient, and absolute errors, calculated for two indices: Visual Field Index (VFI) and Mean Deviation (MD). Results MAC+ONH achieved 0.76 Spearman's correlation coefficient and 0.87 Pearson's correlation for VFI and MD respectively. Median absolute error was 2.7 for VFI and 1.57 dB for MD. Separate MAC or ONH estimates were significantly less correlated and less accurate. Accuracy was dependent on OCT signal strength and on the stage of glaucoma severity. Conclusions The accuracy of global visual field indices estimate is improved by integrating information from MAC and ONH in advanced glaucoma, suggesting that structural changes of the two regions have different timecourses in the disease severity spectrum.