Development of Cumulative Order-Preserving Image Transformation Based Variational Autoencoder for Anterior Segment Optical Coherence Tomography Images.

Purpose: To develop a variational autoencoder (VAE) suitable for analysis of the latent structure of anterior segment optical coherence tomography (AS-OCT) images and to investigate possibilities of latent structure analysis of the AS-OCT images. Methods: We retrospectively collected clinical data and AS-OCT images from 2111 eyes of 1261 participants from the ongoing Asan Glaucoma Progression Study. A specifically modified VAE was used to extract six symmetrical and one asymmetrical latent variable. A total of 1692 eyes of 1007 patients were used for training the model. Conventional measurements and latent variables were compared between 74 primary angle closure (PAC) and 51 primary angle closure glaucoma (PACG) eyes from validation set (419 eyes of 254 patients) that were not used for training. Results: Among the symmetrical latent variables, the first three and the last demonstrated easily recognized features, anterior chamber area in η1, curvature of the cornea in η2, the pupil size in η3 and corneal thickness in η6, whereas η4 and η5 were more complex aggregating complex interactions of multiple structures. Compared with PAC eyes, there was no difference in any of the conventional measurements in PACG eyes. However, values of η4 were significantly different between the two groups, being smaller in the PACG group (P = 0.015). Conclusions: VAE is a useful framework for analysis of the latent structure of AS-OCT. Latent structure analysis could be useful in capturing features not readily evident with conventional measures. Translational Relevance: This study suggested that a deep learning-based latent space model can be applied for the analysis of AS-OCT images to find latent characteristics of the anterior segment of the eye.