Deep learning-assisted widefield endothelial imaging in Descemet membrane endothelial keratoplasty.

Purpose: Significant endothelial cell density (ECD) losses in Descemet membrane endothelial keratoplasty (DMEK) can precipitate graft failure. Currently, clinical practice only evaluates the central corneal endothelium and not the paracentral or peripheral cornea. Here, we evaluate a deep learning (DL) algorithm for automated image quality assessment and detection of low corneal ECD in widefield specular microscopy (WFSM) images following DMEK, at central, paracentral and peripheral regions. Methods: Prospective observational study in eyes with clear, stable DMEKs. WFSM imaging (CEM-530; Nidek, Japan) performed at a baseline visit (mean of 23 months post-DMEK) and 6 months after. Images at 15 locations were captured; 1 central, 8 paracentral, and 6 peripheral. Image quality and corneal endothelial cell parameters including ECD were assessed. SqueezeNet architecture was employed for binary and multi-class automated image quality and low ECD detection (threshold set at < 1,000 cells/mm Results: DMEK was performed in 53 eyes (43 subjects, mean age 67.4 ± 10.1 years) with Fuchs endothelial corneal dystrophy (FECD) (71.7%; 38 eyes) and pseudophakic bullous keratopathy (PBK) (28.3%; 15 eyes). The DL classification models were trained and validated on 1,362 images, achieving AUCs of 0.979 (binary image quality), 0.907 (multi-class image quality) and 0.980 (low ECD detection). WFSM imaging was able to detect both central ECD loss (1,709 vs. 1,555 cells/mm Conclusion: Serial WFSM was able to image a larger area of the endothelium in eyes with DMEK. Our DL model was a useful adjunct that analysed the large number of specular microscopy images to identify scans of adequate quality and ECD based on pre-defined thresholds. This approach may support the use of DL-assisted WFSM for early detection of ECD loss which may aid in monitoring endothelial health and graft survival following DMEK.