Journal of Periodontal and Implant Science

Korean Academy of Periodontology (대한치주과학회)
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Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis

  • Akhilanand Chaurasia (Department of Oral Medicine & Radiology, King George's Medical University) ;
  • Arunkumar Namachivayam (Department of Biostatistics, Bapuji Dental College & Hospital) ;
  • Revan Birke Koca-Unsal (Department of Periodontology, Faculty of Dentistry, University of Kyrenia) ;
  • Jae-Hong Lee (Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University)
  • Received : 2022.12.09
  • Accepted : 2023.02.21
  • Published : 2024.02.28
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Abstract

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

Keywords

Acknowledgement

This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2C1083978) and the Fund of Biomedical Research Institute, Jeonbuk National University Hospital.

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