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 (Reg. No.: CRD42021249627). 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 were 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.

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and willimprove the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. Continuous...

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