Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

Zhang, Lang; Li, Wang; Lv, Jinxun; Xu, Jiajie; Zhou, Hengyu; Li, Gen; Ai, Keqi

doi:10.1016/j.jdent.2023.104727

Cited by 4 publications

(1 citation statement)

References 95 publications

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“…Previously, several review papers have investigated MC segmentation. Zhang et al [35] offered an overview of research on segmentation algorithms pertaining to teeth, MC, and the alveolar bone in oral and maxillofacial surgery. However, this paper does not extensively discuss the application of deep learning models for MC segmentation.…”

Section: Introductionmentioning

confidence: 99%

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

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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Context: 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. Objective: 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. Method: This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. Results: 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. Conclusion: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.

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Section: Introductionmentioning

confidence: 99%

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

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis

Xiang,

Lu,

2024

Journal of Dentistry

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Accuracy of artificial intelligence‐based segmentation of the mandibular canal in CBCT

Ntovas,

Marchand,

Finkelman

et al. 2024

Clinical Oral Implants Res

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ObjectivesTo investigate the accuracy of artificial intelligence (AI)‐based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software.Materials and methodsLocalization of the mandibular canals was performed for 104 randomly selected patients. A localization was performed by three experienced clinicians in order to serve as control. Five tracings were performed: One from a clinician with a moderate experience with a manual tracing (I1), followed by the implementation of an automatic refinement (I2), one manual from a dental student (S1), and one from the experienced clinician, followed by an automatic refinement (E). Subsequently, two fully automatic AI‐driven segmentations were performed (A1,A2). The accuracy between each method was measured using root mean square error calculation.ResultsThe discrepancy among the models of the mandibular canals, between the experienced clinicians and each investigated method ranged from 0.21 to 7.65 mm with a mean of 3.5 mm RMS error. The analysis of each separate mandibular canal's section revealed that mean RMS error was higher in the posterior and anterior loop compared to the middle section. Regarding time efficiency, tracing by experienced users required more time compared to AI‐driven segmentation.ConclusionsThe experience of the clinician had a significant influence on the accuracy of mandibular canal's localization. An AI‐driven segmentation of the mandibular canal constitutes a time‐efficient and reliable procedure for pre‐operative implant planning. Nevertheless, AI‐based segmentation results should always be verified, as a subsequent manual refinement of the initial segmentation may be required to avoid clinical significant errors.

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Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

Cited by 4 publications

References 95 publications

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

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

Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis

Accuracy of artificial intelligence‐based segmentation of the mandibular canal in CBCT

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