Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography

Takebe, Katsuki; Imai, Tomoaki; Kubota, Shiro; Nishimoto, Ayano; Amekawa, Shigeki; Uzawa, Narikazu

doi:10.1016/j.jds.2022.12.008

Cited by 13 publications

(8 citation statements)

References 37 publications

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“… Yoo et al (2021) determined the sensitivity and specificity using positive predictive value (PPV) calculations. Furthermore, the studies reviewed in this work did not use a consistent mathematical formula ( Ariji et al, 2022 , Buyuk et al, 2022 , Celik, 2022 , Choi et al, 2022 , Maruta et al, 2023 , Takebe et al, 2022 , Vinayahalingam et al, 2019 , Yoo et al, 2021 , Zhu et al, 2021 ). Celik, 2022 , Choi et al, 2022 , Takebe et al, 2022 , and Zhu et al (2021) , used PPV calculations to determine precision in their research.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the studies reviewed in this work did not use a consistent mathematical formula ( Ariji et al, 2022 , Buyuk et al, 2022 , Celik, 2022 , Choi et al, 2022 , Maruta et al, 2023 , Takebe et al, 2022 , Vinayahalingam et al, 2019 , Yoo et al, 2021 , Zhu et al, 2021 ). Celik, 2022 , Choi et al, 2022 , Takebe et al, 2022 , and Zhu et al (2021) , used PPV calculations to determine precision in their research. A sensitivity calculation is used by Zhu et al (2021) , Takebe et al (2022) and Choi et al (2022) .…”

Section: Discussionmentioning

confidence: 99%

“… Celik, 2022 , Choi et al, 2022 , Takebe et al, 2022 , and Zhu et al (2021) , used PPV calculations to determine precision in their research. A sensitivity calculation is used by Zhu et al (2021) , Takebe et al (2022) and Choi et al (2022) . Meanwhile, Celik (2022) used the true positive formula divided by all the data as a recall.…”

Section: Discussionmentioning

confidence: 99%

“…Technology advancements have enabled the integration of AI into CAD systems, which can be used to assess the relationship between the third molars on either side (bucco-lingual) of the mandible and the mandibular canal. GoogLeNet, VGG-16, AlexNet ( Buyuk et al, 2022 , Fukuda et al, 2020 ), YOLOv3 ( Takebe et al, 2022 ), YOLOv4 ( Zhu et al, 2021 ), ResNet-50 ( Choi et al, 2022 , Sukegawa et al, 2022b ) are some deep learning architectures that have been investigated for radiographic evaluation of impacted third molar related to the mandibular canal. In addition, U-Net is also used for image segmentation of the mandibular canal ( Ariji et al, 2022 , Buyuk et al, 2022 , Vinayahalingam et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, CBCT is expensive and requires larger doses of radiation, thus, panoramic radiography remains a widely used an alternative in the examination of impacted teeth ( Whaites and Drage, 2014 ), in addition as a preliminary step to determining whether CBCT imaging is necessary. In diagnosis, deep learning may be used in conjunction with CBCT ( Takebe et al, 2022 ). However, deep learning improves the visualization of the pseudo contact of ITM with the MC on panoramic radiographs.…”

Section: Discussionmentioning

confidence: 99%

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The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Faadiya,

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et al. 2024

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Faadiya,

Widyaningrum,

Arindra

et al. 2024

The Saudi Dental Journal

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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

Chen,

Dhawan,

Liu

et al. 2024

Clinical & Exp Dental Res

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ObjectivesArtificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision‐making in clinical dentistry and identify trends and research gaps in the current literature.Material and MethodsThis review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Extension for Scoping Reviews (PRISMA‐ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full‐text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.ResultsOf the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry ‐ particularly oral medicine, oral surgery, and orthodontics ‐ for direct clinical inference and segmentation. AI‐based image analysis was use in several components of the clinical decision‐making process, including diagnosis, detection or classification, prediction, and management.ConclusionsA variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

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Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography

Güller,

Kumbasar,

Miloğlu

2024

Oral Radiol

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Deep learning model for the automated evaluation of contact between the lower third molar and inferior alveolar nerve on panoramic radiography

Cited by 13 publications

References 37 publications

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography

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