Individualized Estimation of Baseline Retinal Nerve Fiber Layer Thickness Using Conditional Variational Autoencoder.

Purpose: Use generative deep learning (DL) models to estimate baseline reference nerve fiber layer thickness (NFLT) profiles, taking into account individual ocular characteristics. Design: A cross-sectional study. Participants: Six hundred eighty-six individuals from the Hong Kong FAMILY cohort and 75 individuals from the Casey Eye Institute (CEI) cohort. Methods: Healthy eyes were selected from the Hong Kong FAMILY and CEI cohorts. Circumpapillary NFLT profiles and vascular patterns were measured by a spectral-domain OCT. Generative DL models were trained using the FAMILY data to reconstruct the individualized baseline NFLT, a customized normal reference based on each eye's own vascular pattern, axial length (AL), spherical equivalent (SE) refractive error, disc size, and demographic information. Two DL models were developed. The MAG model used actual AL and SE, while the REG model estimated AL and SE using vascular patterns as input. For comparison, a multiple linear regression (MLR) was trained to estimate baseline NFLT using AL and demographic information. Fivefold cross-validation was used to assess performance. Main Outcome Measures: The prediction error: root-mean-square of the difference between the actual NFLT profile and the predicted individualized baseline. Results: A total of 1152 healthy eyes from 686 participants in the Hong Kong Family cohort were divided into 4 subgroups: high myopia (SE <-6 diopters [D]), low myopia (SE = -6 D ∼ -1 D), emmetropia (SE = -1D∼1D), and hyperopia (SE >1D). Compared with the population means, both DL models significantly reduced the prediction error for overall and quadrant NFLT and decreased the false-positive rate of identifying abnormal NFLT thinning in both myopia groups (from 13.0%-27.0% to 6.3%∼9.4%). Both DL models significantly reduced prediction error for the NFLT profiles compared with both the population mean and the MLR-adjusted NFLT. The reductions in prediction errors for NFLT profile and overall NFLT value were independently validated using the CEI data. Conclusions: Generative DL models (a type of artificial intelligence) can construct individualized NFLT baseline profiles using the vascular pattern derived from the same OCT scans. The individualized baseline reduced the prediction error of the NFLT profile in healthy eyes and may improve the accuracy of identifying abnormal NFLT thinning, especially in myopic eyes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.