Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography

Choi, Eun‐Hye; Lee, Soo−Hong; Jeong, Eunjae; Shin, Seokwon; Park, Hyun-Woo; Youm, Sekyoung; Son, Youngdoo; Pang, Kang-Mi

doi:10.1038/s41598-022-06483-2

Cited by 34 publications

(37 citation statements)

References 35 publications

(33 reference statements)

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“…reported a classi cation accuracy of 0.723. [29] The accuracy of the deep learning model in this study was 0.766, which is almost the same as the classi cation accuracy. In addition, the diagnostic accuracy of specialists was 0.55 to 0.72 (average 0.63), and it was di cult for even specialists to evaluate the continuity between the inferior alveolar nerve and the mandibular third molar using only panoramic radiographic images.…”

Section: Discussionsupporting

confidence: 59%

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Sukegawa

Tanaka

Hara

et al. 2022

Preprint

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In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography (CT) as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent (SGD) and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results demonstrated that ResNet50v2 using the optimizer SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860, 0.890 for the contact analyses and 0.766, 0.843 for the continuity analyses. In contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.

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Section: Discussionsupporting

confidence: 59%

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Sukegawa

Tanaka

Hara

et al. 2022

Preprint

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“…All studies were observational studies except four [ 15 , 16 , 17 , 18 ], which were experimental studies. The types of observational studies included were a mixture of descriptive studies (case report, case series, cross-sectional, qualitative studies) and analytic studies (cohort, case-control).…”

Section: Resultsmentioning

confidence: 99%

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

et al. 2023

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Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners’ clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.

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“…Analysis using deep learning was performed to determine the 3D positional relationships from panoramic images without using other imaging devices. In continuity analysis, although it is difficult to simply compare performance in deep learning studies conducted on different data, a deep learning classification study conducted on 571 images by Choi et al reported a classification accuracy of 0.723 32 . The accuracy of the deep learning model in this study was 0.766, which is almost the same as the classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Sukegawa

Tanaka

Hara

et al. 2022

Sci Rep

View full text Add to dashboard Cite

In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.

show abstract

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography

Cited by 34 publications

References 35 publications

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

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