Artificial intelligence in corneal endothelial disease and endothelial keratoplasty: current evidence, clinical readiness, and future directions
TL;DR
This review outlines the current requirements for clinical translation: multicenter external validation, transparent reporting, safe abstention, and prospective clinical impact studies.
Abstract
Corneal endothelial disease generates high-dimensional imaging and longitudinal data, making it well suited to artificial intelligence (AI). This review follows AI across the endothelial care pathway and judges each application by translational maturity rather than discrimination alone, drawing on a targeted PubMed/MEDLINE search of studies published between 2011 and July 2026. Technical performance is strongest when the task is measurement against a reproducible human reference: endothelial cell segmentation, image quality control, edema and pachymetric mapping, and quantification of Descemet membrane endothelial keratoplasty (DMEK) detachment. However, performance declines as tasks become prognostic or when models cross devices, surgeons, and centers. External validation reduced the area under the curve from 0.96 to 0.77 in one Fuchs endothelial corneal dystrophy (FECD) detection model, and a 2025 systematic review identified external validation in only three of 19 FECD studies. Calibration, uncertainty quantification, decision curve analysis, and prospective human–AI comparison remain uncommon. The most mature applications are therefore measurement and quality-control tasks, including automated donor endothelial cell density assessment in eye banking. FECD staging, prediction of decompensation after cataract surgery, the need for rebubbling (reinjection of air or gas into the anterior chamber to promote graft reattachment), rejection, and long-term graft survival remain investigational. By covering the full clinical pathway together with eye banking, regenerative therapies, foundation models, language models, an explicit clinical-readiness framework, and the European regulatory context, this review outlines the current requirements for clinical translation: multicenter external validation, transparent reporting, safe abstention, and prospective clinical impact studies.