MobileNetV2 Based Diagnosis and Grading of Limbal Stem Cell Deficiency

The limbus is the junction between the cornea and the sclera and harbors adult stem cells involved in corneal epithelial cells renewal, namely the limbal stem cells (LSCs). Damages to the limbus and/or the LSCs will lead to a loss of the limbal functions, defining limbal stem cell deficiency (LSCD). Clinically, LSCD presents with conjunctival cell invasion into the cornea, corneal scarring, neovascularization, and potential blindness. Recent guidelines have clarified the staging of the disease based on the area involved on the cornea and the use of additional imaging such as in vivo confocal microscopy (IVCM). Several biomarkers of LSC function are evaluated by IVCM on the central cornea, including the basal cell density and morphology, and subbasal nerve density. Evaluation of these biomarkers is time-consuming and remains subjective. Therefore, using a deep learning approach could enhance our diagnosis strategy and efficiency. The current paper demonstrates the performance of a deep learning-based pipeline to first identify clinically relevant images from IVCM volume scans and subsequently use them to classify LSCD severity based on a published grading system. The classification model achieved a test-time accuracy of 74%, and areas under the receiver operating characteristic curve (AUROC) of 0.93, 0.93, 0.94, and 0.87 for control, mild, moderate, and severe, respectively. In order to demonstrate the need to use both cell and nerve images, we also compare models that only use cell scans (68% accuracy) and only nerve scans (69% accuracy). This pilot study shows that the diagnosis of LSCD could be automated to improve diagnostic efficiency and to reduce inter- and intraclinician variability of the process.