To evaluate a deep learning–based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT).
In this study, a total of 1172 AS-OCT images (609: attached graft; 563: detached graft) were used to train and test a deep convolutional neural network to automatically detect GD after DMEK surgery in AS-OCT images. GD was defined as a not completely attached graft. After training with 1072 of these images (559: attached graft; 513: detached graft), the created classifier was tested with the remaining 100 AS-OCT scans (50: attached graft; 50 detached: graft). Hereby, a probability score for GD (GD score) was determined for each of the tested OCT images.
The mean GD score was 0.88 ± 0.2 in the GD group and 0.08 ± 0.13 in the group with an attached graft. The differences between both groups were highly significant (P < 0.001). The sensitivity of the classifier was 98%, the specificity 94%, and the accuracy 96%. The coefficient of variation was 3.28 ± 6.90% for the GD group and 2.82 ± 3.81% for the graft attachment group.
With the presented deep learning-based classifier, reliable automated detection of GD after DMEK is possible. Further work is needed to incorporate information about the size and position of GD and to develop a standardized approach regarding when rebubbling may be needed.
Department of Ophthalmology, University of Muenster Medical Center, Muenster, Germany.
Correspondence: Maximilian Treder, MD, Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149 Muenster, Germany (e-mail: email@example.com).
The authors have no funding or conflicts of interest to disclose.
Received July 09, 2018
Accepted August 22, 2018