Early detection of at-risk keratoplasties and prediction of future corneal graft rejection from pre-diagnosis endothelial cell images

The status of the donor tissue post-keratoplasty (post-transplant), whether full or partial thickness, is currently assessed for health, function, and complications via clinical evaluations. This includes detection of visible signs of graft rejection on slit lamp biomicroscopy such as keratic precipitates or edema. Corneal endothelial cell (EC) images are utilized to indirectly assess the health of the cornea post keratoplasty with evidence that morphometric changes may occur prior to clinical signs of rejection. We extracted over 190 novel quantitative features from EC images acquired 1-12 months prior to patientś rejection diagnosis date, and used random forest (RF) classifiers to predict future rejection. We automatically segmented the cell borders of 171 EC images using a semi-automated segmentation approach: deep learning U-Net segmentation followed by guided manual correction. Following segmentation, we extracted novel quantitative features that robustly represented the cellular morphology from the EC images. We trained and tested a RF classifier using 5-fold cross validation and minimal Redundancy Maximal Relevance (mRMR) feature selection. From the 5-fold cross validation, we report an area under the receiver operating characteristic curve (AUC) of 0.87 ± 0.03, a sensitivity of 0.86 ± 0.12, and a specificity of 0.86 ± 0.10. The results suggest we can accurately predict a patient's future graft rejection 1- 12 months prior to diagnosis, enabling clinicians to intervene modifying and/or instituting topical corticosteroid therapy earlier with the possibility of lowering graft rejection failures. Success of this classifier could reduce health care costs, patient discomfort, vision loss and the need for repeat keratoplasty.