Abstract
PURPOSE: Infectious keratitis, especially viral keratitis (VK), in resource-limited settings, can be a challenge to diagnose and carries a high risk of misdiagnosis contributing to significant ocular morbidity. WE AIMED TO: employ and study the application of artificial intelligence-based deep learning (DL) algorithms to diagnose VK. METHODS: A single-center retrospective study was conducted in a tertiary care center from January 2017 to December 2019 employing DL algorithm to diagnose VK from slit-lamp (SL) photographs. Three hundred and seven diffusely illuminated SL photographs from 285 eyes with polymerase chain reaction-proven herpes simplex viral stromal necrotizing keratitis (HSVNK) and culture-proven nonviral keratitis (NVK) were included. Patients having only HSV epithelial dendrites, endothelitis, mixed infection, and those with no SL photographs were excluded. DenseNet is a convolutional neural network, and the two main image datasets were divided into two subsets, one for training and the other for testing the algorithm. The performance of DenseNet was also compared with ResNet and Inception. Sensitivity, specificity, receiver operating characteristic (ROC) curve, and the area under the curve (AUC) were calculated. RESULTS: The accuracy of DenseNet on the test dataset was 72%, and it performed better than ResNet and Inception in the given task. The AUC for HSVNK was 0.73 with a sensitivity of 69.6% and specificity of 76.5%. The results were also validated using gradient-weighted class activation mapping (Grad-CAM), which successfully visualized the regions of input, which are significant for accurate predictions from these DL-based models. CONCLUSION: DL algorithm can be a positive aid to diagnose VK, especially in primary care centers where appropriate laboratory facilities or expert manpower are not available.