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Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnostic perfor...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119725/ https://www.ncbi.nlm.nih.gov/pubmed/33986459 http://dx.doi.org/10.1038/s41598-021-89742-y |
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author | Choi, Eunhye Kim, Donghyun Lee, Jeong-Yun Park, Hee-Kyung |
author_facet | Choi, Eunhye Kim, Donghyun Lee, Jeong-Yun Park, Hee-Kyung |
author_sort | Choi, Eunhye |
collection | PubMed |
description | Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnostic performance from OPGs with that of an oromaxillofacial radiology (OMFR) expert. An AI model was developed using Karas’ ResNet model and trained to classify images into three categories: normal, indeterminate OA, and OA. This study included 1189 OPG images confirmed by cone-beam CT and evaluated the results by model (accuracy, precision, recall, and F1 score) and diagnostic performance (accuracy, sensitivity, and specificity). The model performance was unsatisfying when AI was developed with 3 categories. After the indeterminate OA images were reclassified as normal, OA, or omission, the AI diagnosed TMJOA in a similar manner to an expert and was in most accord with CBCT when the indeterminate OA category was omitted (accuracy: 0.78, sensitivity: 0.73, and specificity: 0.82). Our deep learning model showed a sensitivity equivalent to that of an expert, with a better balance between sensitivity and specificity, which implies that AI can play an important role in primary diagnosis of TMJOA from OPGs in most general practice clinics where OMFR experts or CT are not available. |
format | Online Article Text |
id | pubmed-8119725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81197252021-05-17 Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram Choi, Eunhye Kim, Donghyun Lee, Jeong-Yun Park, Hee-Kyung Sci Rep Article Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnostic performance from OPGs with that of an oromaxillofacial radiology (OMFR) expert. An AI model was developed using Karas’ ResNet model and trained to classify images into three categories: normal, indeterminate OA, and OA. This study included 1189 OPG images confirmed by cone-beam CT and evaluated the results by model (accuracy, precision, recall, and F1 score) and diagnostic performance (accuracy, sensitivity, and specificity). The model performance was unsatisfying when AI was developed with 3 categories. After the indeterminate OA images were reclassified as normal, OA, or omission, the AI diagnosed TMJOA in a similar manner to an expert and was in most accord with CBCT when the indeterminate OA category was omitted (accuracy: 0.78, sensitivity: 0.73, and specificity: 0.82). Our deep learning model showed a sensitivity equivalent to that of an expert, with a better balance between sensitivity and specificity, which implies that AI can play an important role in primary diagnosis of TMJOA from OPGs in most general practice clinics where OMFR experts or CT are not available. Nature Publishing Group UK 2021-05-13 /pmc/articles/PMC8119725/ /pubmed/33986459 http://dx.doi.org/10.1038/s41598-021-89742-y Text en © The Author(s) 2021 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 Kim, Donghyun Lee, Jeong-Yun Park, Hee-Kyung Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title | Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title_full | Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title_fullStr | Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title_full_unstemmed | Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title_short | Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
title_sort | artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119725/ https://www.ncbi.nlm.nih.gov/pubmed/33986459 http://dx.doi.org/10.1038/s41598-021-89742-y |
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