Automated 2D-3D quantitative analysis of corneal graft detachment post DSAEK based on AS-OCT images

Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK

Purpose

To evaluate a novel deep learning algorithm to distinguish between eyes that may or may not have a graft detachment based on pre-Descemet membrane endothelial keratoplasty (DMEK) anterior segment optical coherence tomography (AS-OCT) images.

Methods

Retrospective cohort study. A multiple-instance learning artificial intelligence (MIL-AI) model using a ResNet-101 backbone was designed. AS-OCT images were split into training and testing sets. The MIL-AI model was trained and validated on the training set. Model performance and heatmaps were calculated from the testing set. Classification performance metrics included F1 score (harmonic mean of recall and precision), specificity, sensitivity, and area under curve (AUC). Finally, MIL-AI performance was compared to manual classification by an experienced ophthalmologist.

Results

In total, 9466 images of 74 eyes (128 images per eye) were included in the study. Images from 50 eyes were used to train and validate the MIL-AI system, while the remaining 24 eyes were used as the test set to determine its performance and generate heatmaps for visualization. The performance metrics on the test set (95% confidence interval) were as follows: F1 score, 0.77 (0.57-0.91); precision, 0.67 (0.44-0.88); specificity, 0.45 (0.15-0.75); sensitivity, 0.92 (0.73-1.00); and AUC, 0.63 (0.52-0.86). MIL-AI performance was more sensitive (92% vs. 31%) but less specific (45% vs. 64%) than the ophthalmologist's performance.

Conclusions

The MIL-AI predicts with high sensitivity the eyes that may have post-DMEK graft detachment requiring rebubbling. Larger-scale clinical trials are warranted to validate the model.

Translational Relevance

MIL-AI models represent an opportunity for implementation in routine DMEK suitability screening.

Graft detachments in endothelial keratoplasty

Graft detachment is the most common complication of endothelial keratoplasty. With the ongoing advancements in the field of endothelial keratoplasty, our understanding of risk factors of graft detachments and its management has been evolving. Various prevention measures have been described in literature including presoaking the donor graft, anterior chamber tamponade, venting incisions, sutures to prevent dislocation of graft. Management of a detached graft involves secondary interventions such as rebubbling, suturing and regrafts. In this review, we discuss graft detachment in different types of endothelial keratoplasty techniques including Descemet stripping endothelial keratoplasty, Descemet stripping automated endothelial keratoplasty and Descemet's membrane endothelial keratoplasty; with emphasis on incidence, risk factors, preventive measures and their management.