Machine Learning Analysis of Postkeratoplasty Endothelial Cell Images for the Prediction of Future Graft Rejection

Abstract

Purpose: This study developed machine learning (ML) classifiers of postoperative corneal endothelial cell images to identify postkeratoplasty patients at risk for allograft rejection within 1 to 24 months of treatment.

Methods: Central corneal endothelium specular microscopic images were obtained from 44 patients after Descemet membrane endothelial keratoplasty (DMEK), half of whom had experienced graft rejection. After deep learning segmentation of images from all patients' last and second-to-last imaging, time points prior to rejection were analyzed (175 and 168, respectively), and 432 quantitative features were extracted assessing cellular spatial arrangements and cell intensity values. Random forest (RF) and logistic regression (LR) models were trained on novel-to-this-application features from single time points, delta-radiomics, and traditional morphometrics (endothelial cell density, coefficient of variation, hexagonality) via 10 iterations of threefold cross-validation. Final assessments were evaluated on a held-out test set.

Results: ML classifiers trained on novel-to-this-application features outperformed those trained on traditional morphometrics for predicting future graft rejection. RF and LR models predicted post-DMEK patients' allograft rejection in the held-out test set with >0.80 accuracy. RF models trained on novel features from second-to-last time points and delta-radiomics predicted post-DMEK patients' rejection with >0.70 accuracy. Cell-graph spatial arrangement, intensity, and shape features were most indicative of graft rejection.

Conclusions: ML classifiers successfully predicted future graft rejections 1 to 24 months prior to clinically apparent rejection. This technology could aid clinicians to identify patients at risk for graft rejection and guide treatment plans accordingly.

Translational relevance: Our software applies ML techniques to clinical images and enhances patient care by detecting preclinical keratoplasty rejection.

Figure 1.

Rejection keratoplasty prediction workflow. Images taken at the second-to-last imaging time point and last imaging time point prior to rejection (T1 and T2, respectively) are segmented by a U-Net deep learning model. Using cell segmentations, 432 features are extracted from each image. The delta-radiomics data set is calculated by taking the difference of the T2 and T1 features and dividing this difference by the time duration between the two sets of images. All three data sets are then split, with two-thirds of patient eyes used for training and one-third of patient eyes for held-out for testing. The training set underwent three feature selection (FS) techniques and 10 iterations of three-fold cross-validation to build RF and LR machine learning (ML) models. The top 3, 5, 7, and 10 features were collected from each of the three time point data sets to train final RF and LR models. Model prediction performance (e.g., accuracy) was evaluated on the patient eyes held-out test set. In total, 120 cross-validation models were trained and 8 final models were developed for postkeratoplasty rejection prediction.

Figure 2.

Example endothelial cell images taken at (a) 1 month, (b) 3 months, (c) 86 months, and (d) 54 months post-Descemet membrane endothelial keratoplasty demonstrating varying cell size, shape, arrangement, and image quality.

Figure 3.

Graphs used to compute cell arrangement features. (a) Example post-Descemet membrane endothelial keratoplasty endothelial cell images with (b) Voronoi tessellation, (c) Delaunay tessellation, (d) cluster graph, and (e) minimum spanning tree feature graph overlays.

Figure 4.

Correlation matrices of the traditional metrics (ECD, CV, and HEX) and the top 10 features determined by data sets collected from (a) the second-to-last imaging time point prior to rejection (T1), (b) the last imaging time point prior to rejection (T2), and (c) delta-radiomics.

Figure 5.

Comparison of Delaunay triangulation feature between rejection (left) and control (right) patients. The two rejection images on the left were taken from a patient's eye 30 months and 36 months post-DMEK. The two control images on the right were taken from a patient's eye 25 and 32 months postkeratoplasty. The green graph arrangements overlaying each image is the corresponding Delaunay triangulation. The violin and box plots compare the distribution of Delaunay triangulation side length average calculated from control and rejection eye images.

Figure 6.

Comparison of a cell-cluster graph feature between rejection (left) and control (right) patients. The two rejection images on the left were taken from the same patient's eye 16 months and 24 months post-DMEK. The two images on the right were taken from the same patient's eye 13 and 24 months post-DMEK. The images are overlaid with cell-cluster graphs. The violin and box plots compare the distribution of number of connected components in a cell-cluster graph calculated from control and rejection eye images.

Figure 7.

Limitations of HEX as a distinguishing feature between rejection and control patients. Both rejection and control eye images showcase the same percent hexagonality. A scatterplot compares two features, Voronoi tessellation area of polygons and cell-cluster graph number of components. The two data points corresponding to the two images are labeled in the scatterplot.

Figure 8.

Limitations of ECD as a distinguishing feature between rejection and control patients. Both rejection and control eye images showcase the same endothelial cell density. A scatterplot compares two features, Delaunay triangulation average triangle area and cell-cluster graph maximum eccentricity. The two data points corresponding to the two images are labeled in the scatterplot.