Characteristics, licensing, and ethical considerations of openly accessible oral-maxillofacial imaging datasets: a systematic review
Jing Hao, Andrew Nalley, Andy Wai Kan Yeung, Ray Tanaka, Qi Yong H. Ai, Walter Yu Hang Lam, Zhiyi Shan, Yiu Yan Leung, Abeer AlHadidi, Michael M. Bornstein, James Kit Hon Tsoi, Colman McGrath, Kuo Feng Hung
Abstract
Several open-source oral-maxillofacial imaging datasets have been created but their characteristics, ethical clearance, and licensing for reuse remain unclear. This study aimed to systematically identify these datasets and investigate their characteristics, ethical approvals, and licensing requirements for reuse. Open-source oral-maxillofacial imaging datasets were identified through electronic databases and dataset platforms. 105 datasets with 437538 images and 100 intraoral videos from patients across twenty-one countries were included. The datasets comprise imaging modalities, including photographs, periapical, panoramic, and cephalometric radiographs, CBCT, MRI, surface scans, videos, and histopathological images. Nearly 80% of them provide annotations, but only 25.7% specified the annotators' qualification. The majority (83.8%) did not disclose whether ethical approval was obtained, while 61.9% specified terms or licenses for dataset reuse. There is an urgent need to develop standardized guidelines for reusing image datasets and to establish AI-specific consents to fully inform patients about potential uses of their data in AI projects.