Application of deep learning in teeth identification tasks on panoramic radiographs

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Abstract
Objectives: To investigate the current developments of Artificial Intelligence (AI) in teeth identification on Panoramic Radiographs (PR). Our aim was to evaluate and compare the performances of Deep Learning (DL) models that have been employed in the execution of this task. Methods: The systematic review was registered on PROSPERO. All recent studies that utilized DL models for identifying teeth on PRs were included in this review. An extensive search of the medical electronic databases including PubMed NLM, EBSCO Dentistry & Oral Sciences Source, and Wiley Cochrane Library was conducted. This was followed by a hand search of the IEEE Xplore database. The diagnostic performance of DL models in teeth identification tasks on PR was the primary outcome assessed in this review. The risk of bias assessment of the included studies was evaluated via the modified QUADAS-2 tool. Owing to the heterogeneity of the reported performance metrics, a meta-analysis was not possible.. Results: The search yielded a total of 282 articles, out of which 13 relevant ones were included in this review. These studies utilized a diverse range of DL models for teeth identification tasks on PRs and reported their performances using a variety of metrics. Conclusion: The results of teeth identification tasks carried out by DL models are encouraging; however, there is a need for the shortcomings that have been identified in our preliminary review, to be addressed by future researchers.

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