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The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review

Author

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  • Julien Issa

    (Department of Biomaterials and Experimental Dentistry, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland)

  • Raphael Olszewski

    (Department of Oral and Maxilofacial Surgery, Cliniques Universitaires Saint Luc, UCLouvain, Av. Hippocrate 10, 1200 Brussels, Belgium
    Oral and Maxillofacial Surgery Research Lab (OMFS Lab), NMSK, Institut de Recherche Experimentale et Clinique, UCLouvain, Louvain-la-Neuve, 1348 Brussels, Belgium
    These authors contributed equally to this work.)

  • Marta Dyszkiewicz-Konwińska

    (Department of Biomaterials and Experimental Dentistry, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland
    These authors contributed equally to this work.)

Abstract

This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.

Suggested Citation

  • Julien Issa & Raphael Olszewski & Marta Dyszkiewicz-Konwińska, 2022. "The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-10, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:1:p:560-:d:717800
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    References listed on IDEAS

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    1. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    2. Kurt Benke & Geza Benke, 2018. "Artificial Intelligence and Big Data in Public Health," IJERPH, MDPI, vol. 15(12), pages 1-9, December.
    3. Kuofeng Hung & Andy Wai Kan Yeung & Ray Tanaka & Michael M. Bornstein, 2020. "Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice," IJERPH, MDPI, vol. 17(12), pages 1-18, June.
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