Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

PURPOSE: To develop a novel deep-learning approach that can describe the structural phenotype of the glaucomatous optic nerve head (ONH) and can be used as a robust glaucoma diagnosis tool. DESIGN: Retrospective, deep-learning approach diagnosis study. METHOD: We trained a deep-learning network to segment 3 neural-tissue and 4 connective-tissue layers of the ONH. The segmented optical coherence tomography images were then processed by a customized autoencoder network with an additional parallel branch for binary classification. The encoder part of the autoencoder reduced the segmented optical coherence tomography images into a low-dimensional latent space (LS), whereas the decoder and the classification branches reconstructed the images and classified them as glaucoma or nonglaucoma, respectively. We performed principal component analysis on the latent parameters and identified the principal components (PCs). Subsequently, the magnitude of each PC was altered in steps and reported how it impacted the morphology of the ONH. RESULTS: The image reconstruction quality and diagnostic accuracy increased with the size of the LS. With 54 parameters in the LS, the diagnostic accuracy was 92.0 +/- 2.3% with a sensitivity of 90.0 +/- 2.4% (at 95% specificity), and the corresponding Dice coefficient for the reconstructed images was 0.86 +/- 0.04. By changing the magnitudes of PC in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a nonglaucoma to a glaucoma condition. CONCLUSIONS: Our network was able to identify novel biomarkers of the ONH for glaucoma diagnosis. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study develops a novel deep-learning approach for describing the structural phenotype of the glaucomatous optic nerve head (ONH) and serving as a robust glaucoma diagnosis tool. By analyzing the latent parameters, it reveals the changes in ONH morphology during the transition from a non-glaucoma to a glaucoma condition.