Deep Learning-Based Detection of Ocular Surface Squamous Neoplasia from Ocular Surface Images
Obaidur Rehman, Ramkailash Gujar, R. K. Kumawat, Ruby Pandey, Chhavi Gupta, Shweta Tiwari, Veena Sangwan, Sima Das
Abstract
Introduction: Ocular surface squamous neoplasia (OSSN) is a broad entity encompassing a spectrum of squamous neoplasms of conjunctiva and cornea. This study aimed to explore the utility of artificial intelligence (AI) models in detecting OSSN from slit-lamp (SL) images. Methods: ) were collected (2013-2023). Images with minimum resolution of 1,024 × 1,024 pixels under diffuse illumination were included. Data were divided into training and testing sets (85:15). Deep learning (DL) algorithms were applied for ternary classification of the SL images (OSSN, OOSD, and normal). Three AI models - MobileNetV2, Xception, and DenseNet121 - were used in the study. A fivefold cross-validation strategy was utilized for robust model evaluation. Results: = 634). Data augmentation was performed to increase and balance the data. The average accuracies for OSSN detection for DenseNet121, MobileNetV2, and Xception were 83%, 88.8%, and 84.5%, respectively. MobileNetV2 and Xception had a similar average sensitivity for OSSN detection (74%) while MobileNetV2 was the most specific DL algorithm (96.25%) for OSSN detection. Conclusions: AI models showed good performance in image-based OSSN detection. AI models may provide a promising tool for OSSN screening in primary health care centers and for teleconsultation from remote areas in the future.