Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Mohammad Farid Naufal, Chastine Fatichah*, Eha Renwi Astuti, Ramadhan Hardani Putra

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Several recent studies in dentistry and maxillofacial imaging have concentrated on the Mandibular Canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, Google Scholar, and SpringerLink. This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

Original languageEnglish
Pages (from-to)76794-76815
Number of pages22
JournalIEEE Access
Publication statusPublished - 2024


  • Mandibular canal
  • deep learning
  • inferior alveolar nerve
  • segmentation
  • systematic literature review

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