Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume

Abstract

To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images. This was a proof-of-concept consecutive case series. Patients with an idiopathic full-thickness MH undergoing pars plana vitrectomy surgery with 1 year of follow-up were considered for inclusion. MHs were manually graded by a vitreoretinal surgeon from preoperative OCT images to delineate MH volume. This information was used to train a fully three-dimensional convolutional neural network for automatic segmentation. The main outcome was the correlation of manual MH volume to automated volume segmentation. The correlation between manual and automated MH volume was R2 = 0.94 (n = 24). Automated MH volume demonstrated a higher correlation to change in visual acuity from preoperative to the postoperative 1-year time point compared with the minimum linear diameter (volume: R2 = 0.53; minimum linear diameter: R2 = 0.39). MH automated volume segmentation on OCT imaging demonstrated high correlation to manual MH volume measurements. [Ophthalmic Surg Lasers Imaging Retina. 2022;53(4):208-214.].