AUTHOR=Statsenko Yauhen , Smetanina Darya , Voitetskii Roman , Simiyu Gillian Lylian , Pazniak Mikalai , Likhorad Elena , Pazniak Aleh , Beliakouski Pavel , Abelski Dmitri , Ismail Fatima , Neidl-Van Gorkom Klaus , Ljubisavljevic Milos 

TITLE=Digital transformation of care for keratoconus patients: ML modeling structural outcomes of corneal collagen cross-linking

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1462653

DOI=10.3389/fmed.2025.1462653

ISSN=2296-858X

ABSTRACT=BackgroundStructural outcomes of corneal collagen cross-linking (CXL) have not been thoroughly investigated. Clinical risk assessment would benefit from a reliable prognosis of postoperative minimal (MCT) and central corneal thickness (CCT).ObjectiveThe objective of this study was to find a combination of diagnostic modalities and measurements that reliably reflect CXL efficiency in terms of corneal thickness.MethodsWe retrospectively reviewed the medical histories of 107 patients (131 eyes) who underwent CXL. The dataset included preoperative examinations and follow-up results, which totalled 796 observations.ResultsThe postoperative changes in MCT are more pronounced, clinically relevant, and meaningful than in CCT. MCT should serve as the major clinical marker of corneal thinning after CXL. The cornea's potential to recover reduces in advanced keratoconus. A polynomial curve demonstrates the natural course of corneal remodeling. It includes thinning immediately after CXL and stabilization with partial recovery of corneal thickness over time. Baseline pachymetry data can adequately reflect the outcomes. Preoperative BAD and topographic indices strongly correlate with the outcomes. Keratometry and refractometry data exhibit moderate associations with postoperative corneal thickness. The models trained on a combination of top correlating features, clinical data, and time after intervention provide the most reliable prognosis.ConclusionRisk assessment is accurate with multimodal preoperative diagnostics. A stratification system should take into account findings in different diagnostic modalities.