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

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to...

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Autores principales: Choi, Eunhye, Lee, Soohong, Jeong, Eunjae, Shin, Seokwon, Park, Hyunwoo, Youm, Sekyoung, Son, Youngdoo, Pang, KangMi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844031/
https://www.ncbi.nlm.nih.gov/pubmed/35165342
http://dx.doi.org/10.1038/s41598-022-06483-2
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author Choi, Eunhye
Lee, Soohong
Jeong, Eunjae
Shin, Seokwon
Park, Hyunwoo
Youm, Sekyoung
Son, Youngdoo
Pang, KangMi
author_facet Choi, Eunhye
Lee, Soohong
Jeong, Eunjae
Shin, Seokwon
Park, Hyunwoo
Youm, Sekyoung
Son, Youngdoo
Pang, KangMi
author_sort Choi, Eunhye
collection PubMed
description Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.
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spelling pubmed-88440312022-02-16 Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography Choi, Eunhye Lee, Soohong Jeong, Eunjae Shin, Seokwon Park, Hyunwoo Youm, Sekyoung Son, Youngdoo Pang, KangMi Sci Rep Article Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment. Nature Publishing Group UK 2022-02-14 /pmc/articles/PMC8844031/ /pubmed/35165342 http://dx.doi.org/10.1038/s41598-022-06483-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Choi, Eunhye
Lee, Soohong
Jeong, Eunjae
Shin, Seokwon
Park, Hyunwoo
Youm, Sekyoung
Son, Youngdoo
Pang, KangMi
Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title_full Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title_fullStr Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title_full_unstemmed Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title_short Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
title_sort artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844031/
https://www.ncbi.nlm.nih.gov/pubmed/35165342
http://dx.doi.org/10.1038/s41598-022-06483-2
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