Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A...

To assess the diagnostic accuracy of artificial intelligence-based models in the determination of tooth extraction in orthodontic treatment planning.

A comprehensive literature search was conducted in multiple databases (PubMed, LILACS, Web of Science, Scopus, EBSCO, and Google Scholar) up to June, 2024. Studies that met the inclusion criteria based on the PIRD (Participants, Index test, Reference test, Diagnostic) framework were selected. The risk of bias of included studies was assessed using the QUADAS-2 tool, and their methodological quality was evaluated as well using a standardized checklist.

Out of 361 retrieved records, eleven studies were included in this review. Nine of these studies achieved a score of over 50% on the AI quality checklist, indicating acceptable methodological quality. However, a comprehensive assessment using the QUADAS-2 tool revealed that all studies had some level of risk of bias, particularly in patient selection, the conduct of AI-based predictions, and the reference standard used.

Neural networks and classifier models demonstrated the high level of accuracy ranging from 82% to 94% in determining the optimal tooth extraction protocol. However, to ensure reliable predictions, artificial intelligence-based models should be rigorously trained, incorporating a comprehensive range of factors.