A Smartphone-based Comprehensive Dataset of Annotated Oral Cavity Images for Enhanced Oral Disease Diagnosis

Abstract This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artificial Intelligence and Machine Learning (AI/ML) technologies for automated diagnosis of Oral Cancer (OC) and Oral Potentially Malignant Disorders (OPMD). The dataset consists of 2,469 images from 331 subjects from four distinct classes: healthy/normal, variations from normal, OPMD, and OC. The images are captured using Android and iOS smartphone cameras under real-world clinical conditions in visible light. Each image is annotated by expert dental surgeons using the open-source VGG image annotator. Elaborate patient metadata, including clinical diagnosis, age, sex, and lifestyle-based risk indicators such as smoking, smokeless tobacco usage, alcohol consumption, and areca nut chewing, are recorded via a customized Jotform. The data collection and handling procedures are adhered to the ethical guidelines outlined in the Declaration of Helsinki and its amendments for research involving human subjects, with informed consent obtained from each subject. The SMART-OM dataset is intended to advance research and development of AI/ML algorithms for automated oral lesion detection.