Current applications and development of artificial intelligence for digital dental radiography

TLDR

Artificial intelligence has rapidly advanced in dental radiology, with dental radiographs providing a rich data source that has attracted extensive research. This review examined the current applicability of AI in dental radiography and recommends adopting standardized reporting formats to accelerate clinical translation. The authors performed systematic searches of PubMed and IEEE Xplore through December 2020, categorized AI applications by clinical purpose, and discussed methodological developments across these domains. While AI shows promise for dental imaging, its clinical deployment remains limited by insufficient dataset sizes and inconsistent reporting standards.

Abstract

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.