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Updated: Sep 23, 2025
Corneal Donor Tissue Preparation for Descemet's Membrane Endothelial Keratoplasty
Published on: September 17, 2014
Transferability of an Artificial Intelligence Algorithm Predicting Rebubblings After Descemet Membrane Endothelial
Takahiko Hayashi1, Rosa M Iliasian2, Mario Matthaei2
1Division of Ophthalmology, Department of Visual Sciences, Nihon University School of Medicine, Itabashi, Tokyo, Japan.
Purpose:
To develop an artificial intelligence (AI) algorithm enabling corneal surgeons to predict the probability of rebubbling after Descemet membrane endothelial keratoplasty (DMEK) from images obtained using optical coherence tomography (OCT).
Methods:
Anterior segment OCT data of patients undergoing DMEK by 2 different DMEK surgeons (C.C. and B.B.; University of Cologne, Cologne, Germany) were extracted from the prospective Cologne DMEK database. An AI algorithm was trained by using a data set of C.C. to detect graft detachments and predict the probability of a rebubbling. The architecture of the AI model used in this study was called EfficientNet. This algorithm was applied to OCT scans of patients, which were operated by B.B. The transferability of this algorithm was analyzed to predict a rebubbling after DMEK.
Results:
The algorithm reached an area under the curve of 0.875 (95% confidence interval: 0.880-0.929). The cutoff value based on the Youden index was 0.214, and the sensitivity and specificity for this value were 78.9% (67.6%-87.7%) and 78.6% (69.5%-86.1%).
Conclusions:
The development of AI algorithms allows good transferability to other surgeons reaching a high accuracy in predicting rebubbling after DMEK based on OCT image data.
