Secondary Logo

Journal Logo

Institutional members access full text with Ovid®

Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty

A Pilot Study

Treder, Maximilian MD; Lauermann, Jost Lennart MD; Alnawaiseh, Maged MD; Eter, Nicole MD

doi: 10.1097/ICO.0000000000001776
Clinical Science
Buy

Purpose: 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).

Methods: 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.

Results: 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.

Conclusions: 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: maximilian.treder@ukmuenster.de).

The authors have no funding or conflicts of interest to disclose.

Received July 09, 2018

Accepted August 22, 2018

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.