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Cornea and External Disease

AI-based methods show high accuracy in prediciting graft failure risks in DMEK

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Machine learning techniques can effectively predict early graft failure (GF) in patients undergoing Descemet membrane endothelial keratoplasty (DMEK), according to a study.

Duration of intensive care unit (ICU) stay and death-to-preservation time (DPT) are significant predictors of GF risk.

The study found that machine learning models achieved a classification accuracy of 96%, with a precision of 0.95, recall of 0.81, and an F1-score of 0.90. The analysis indicated that longer ICU stays and extended DPT were significant predictors of GF risk (P < 0.05), while donor age, endothelial cell density, and other factors did not show a significant relationship with GF.

Reference
Karaca EE, Bulut Ustael A, Keçeli AS, et al. Predicting Success in Descemet Membrane Endothelial Keratoplasty Surgery Using Machine Learning. Cornea. 2024;doi: 10.1097/ICO.0000000000003599. Epub ahead of print. PMID: 38913970.

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