Hayashi Takahiko, Iliasian Rosa M, Matthaei Mario, Schrittenlocher Silvia, Masumoto Hiroki, Tanabe Mao, Tabuchi Hitoshi, Siggel Robert, Bachmann Björn, Cursiefen Claus, Siebelmann Sebastian
Division of Ophthalmology, Department of Visual Sciences, Nihon University School of Medicine, Itabashi, Tokyo, Japan.
Department of Ophthalmology, University of Cologne, Cologne, Germany.
Cornea. 2023 May 1;42(5):544-548. doi: 10.1097/ICO.0000000000003049. Epub 2022 Apr 21.
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).
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.
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%).
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.
开发一种人工智能（AI）算法，使角膜外科医生能够根据光学相干断层扫描（OCT）获得的图像预测Descemet膜内皮角膜移植术（DMEK）后气泡再形成的概率。
从前瞻性的科隆DMEK数据库中提取由2位不同的DMEK外科医生（C.C.和B.B.；德国科隆大学）进行DMEK手术患者的眼前节OCT数据。使用C.C.的数据集训练一种AI算法，以检测移植物脱离并预测气泡再形成的概率。本研究中使用的AI模型架构称为EfficientNet。将该算法应用于由B.B.操作的患者的OCT扫描。分析该算法的可转移性，以预测DMEK后的气泡再形成情况。
该算法的曲线下面积达到0.875（95%置信区间：0.880 - 0.929）。基于约登指数的临界值为0.214，该值的敏感性和特异性分别为78.9%（67.6% - 87.7%）和78.6%（69.5% - 86.1%）。
AI算法的开发具有良好的可转移性，能够基于OCT图像数据在预测DMEK后气泡再形成方面达到较高的准确性。
