Artificial Intelligence Aided Analysis of Anterior Segment Optical Coherence Tomography Imaging to Monitor the Device-Cornea Joint After Synthetic Cornea Implantation.

Abstract

Purpose: The purpose of this study was to assess the utility of artificial intelligence (AI) assisted analysis of anterior segment optical coherence tomography (AS-OCT) imaging of the device-cornea joint in predicting outcomes of an intrastromal synthetic cornea device in a rabbit model.

Methods: Sixteen rabbits underwent intrastromal synthetic cornea implantation. Baseline anterior lamellar thickness was established using AS-OCT intraoperatively. Monthly postoperative clinical examinations and AS-OCT imaging were performed, focusing on the peri-optic zone. A convolutional neural network was trained using a subset of manually marked images to automatically detect anterior lamellar tissue. Images were aligned manually using reference coordinates. The tissue volume data were evaluated as both absolute volume and percentage change from baseline using AI.

Results: Sixteen rabbits were observed for 6 (n = 8) and 12 (n = 8) months. Mild focal anterior lamella thinning without retraction was seen near tight sutures in 2 rabbits (2/8) in the 6-month cohort, whereas 2 rabbits (2/8) in the 12-month cohort showed mild focal retraction from the optic stem with thinning. AI-assisted AS-OCT image analyses detected tissue volume reduction up to 3 months before clinical examination, with a reliable threshold of 5% change in tissue volume.

Conclusions: AI-assisted AS-OCT can detect peri-prosthetic tissue loss and predicting postoperative complications following an intrastromal synthetic cornea implantation in a rabbit model. Further studies are warranted to explore its clinical utility in human patients.

Translational relevance: AI-assisted monitoring of peri-optic corneal tissue volume may be a useful screening modality to detect subclinical thinning after artificial corneal implantation and inform clinical decision making.