Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review

Abstract

Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.

Keywords: Artificial intelligence; CNN; Radiographic images; Tooth detection; Tooth numbering.

Fig. 1

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources.

Fig. 2

QUADAS-2 assessment of the individual risk of bias domains and applicability.