Assessing Surface Shapes of the Optic Nerve Head and Peripapillary Retinal Nerve Fiber Layer in Glaucoma with Artificial Intelligence

PURPOSE: To assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI). DESIGN: Retrospective study. PARTICIPANTS: Patients with OCT scans obtained between 2016 and 2020 from Massachusetts Eye and Ear. METHODS: The first reliable Cirrus (Carl Zeiss Meditec, Inc) ONH OCT scans from each eye were selected. The ONH and RNFL surface shape was represented by the vertical positions of the inner limiting membrane (ILM) relative to the lowest ILM vertical position in each eye. Nonnegative matrix factorization was applied to determine the ONH and RNFL surface shape patterns, which then were correlated with OCT and visual field (VF) loss parameters and subsequent VF loss rate. We tested whether using ONH and RNFL surface shape patterns improved the prediction accuracy for associated VF loss and subsequent VF loss rates measured by adjusted r(2) and Bayesian information criterion (BIC) difference compared with using established OCT parameters alone. MAIN OUTCOME MEASURES: Optic nerve head and RNFL surface shape patterns and prediction of the associated VF loss and subsequent VF loss rates. RESULTS: We determined 14 ONH and RNFL surface shape patterns using 9854 OCT scans from 5912 participants. Worse mean deviation (MD) was most correlated (r = 0.29 and r = 0.24, Pearson correlation; each P < 0.001) with lower coefficients of patterns 10 and 12 representing inferior and superior para-ONH nerve thinning, respectively. Worse MD was associated most with higher coefficients of patterns 5, 4, and 9 (r = –0.16, r = –0.13, and r = –0.13, respectively), representing higher peripheral ONH and RNFL surfaces. In addition to established ONH summary parameters and 12–clock-hour RNFL thickness, using ONH and RNFL surface patterns improved (BIC decrease: 182, 144, and 101, respectively; BIC decrease ≥ 6; strong model improvement) the prediction of accompanied MD (r(2) from 0.32 to 0.37), superior (r(2) from 0.27 to 0.31), and inferior (r(2) from 0.17 to 0.21) paracentral loss and improved (BIC decrease: 8 and 8, respectively) the prediction of subsequent VF MD loss rates (r(2) from 0 to 0.13) and inferior paracentral loss rates (r(2) from 0 to 0.16). CONCLUSIONS: The ONH and RNFL surface shape patterns quantified by unsupervised AI techniques improved the structure–function relationship and subsequent VF loss rate prediction.