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Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence

Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-c...

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Autores principales: Lee, Sangyeon, Kim, Donghyun, Jeong, Ho-Gul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956729/
https://www.ncbi.nlm.nih.gov/pubmed/35338198
http://dx.doi.org/10.1038/s41598-022-09083-2
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author Lee, Sangyeon
Kim, Donghyun
Jeong, Ho-Gul
author_facet Lee, Sangyeon
Kim, Donghyun
Jeong, Ho-Gul
author_sort Lee, Sangyeon
collection PubMed
description Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians’ time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients’ dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians.
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spelling pubmed-89567292022-03-30 Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence Lee, Sangyeon Kim, Donghyun Jeong, Ho-Gul Sci Rep Article Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians’ time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients’ dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians. Nature Publishing Group UK 2022-03-25 /pmc/articles/PMC8956729/ /pubmed/35338198 http://dx.doi.org/10.1038/s41598-022-09083-2 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
Lee, Sangyeon
Kim, Donghyun
Jeong, Ho-Gul
Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title_full Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title_fullStr Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title_full_unstemmed Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title_short Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
title_sort detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956729/
https://www.ncbi.nlm.nih.gov/pubmed/35338198
http://dx.doi.org/10.1038/s41598-022-09083-2
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