Classification of the Relationship Between Mandibular Third Molar and Inferior Alveolar Nerve Based on Generated Mask Images

Joo, Yunsang; Moon, Seong-Yong; Choi, Chang

doi:10.1109/access.2023.3302271

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…The Swin U-Net is specifically developed to overcome the constraints of CNNs in acquiring comprehensive semantic knowledge over the whole input, which could be improved by the localized nature of convolution processes. The Swin U-Net model has been used to perform MC segmentation on panoramic radiographs images [77].…”

Section: ) Swin U-netmentioning

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: ) Swin U-netmentioning

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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“…Consequently, the accurate assessment capability of panoramic radiographs in determining true contact relationships, avoiding misinterpretation issues, and thereby reducing the frequency of CT usage is of paramount importance. In a previous study [19], a classifier was introduced to discern the relationship between the inferior alveolar nerve and impacted teeth on DPR images, ultimately achieving an accuracy of 79.5% and a mean average precision (mAP) of 0.885.…”

Section: Introductionmentioning

confidence: 99%

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

Chen,

Chou,

Chuo

et al. 2024

Electronics

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In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.

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System for automatically assessing the likelihood of inferior alveolar nerve injury

Gong,

Feng,

et al. 2024

Computers in Biology and Medicine

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Classification of the Relationship Between Mandibular Third Molar and Inferior Alveolar Nerve Based on Generated Mask Images

Cited by 4 publications

References 44 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

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

System for automatically assessing the likelihood of inferior alveolar nerve injury

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