Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
Abstract 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 diagnost...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-89742-y |
| _version_ | 1818840812416401408 |
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| author | Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park |
| author_facet | Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park |
| author_sort | Eunhye Choi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-19T04:16:08Z |
| format | Article |
| id | doaj.art-0ef6c1e8f22646428af26f5ae997cd3d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T04:16:08Z |
| publishDate | 2021-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0ef6c1e8f22646428af26f5ae997cd3d2022-12-21T20:36:17ZengNature PortfolioScientific Reports2045-23222021-05-011111710.1038/s41598-021-89742-yArtificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogramEunhye Choi0Donghyun Kim1Jeong-Yun Lee2Hee-Kyung Park3Department of Oral Medicine and Oral Diagnosis, School of Dentistry and Dental Research Institute, Seoul National UniversityDepartment of Advanced General Dentistry, Yonsei University College of DentistrySeoul Cheongchoon Dental ClinicDepartment of Oral Medicine and Oral Diagnosis, School of Dentistry and Dental Research Institute, Seoul National UniversityAbstract 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.https://doi.org/10.1038/s41598-021-89742-y |
| spellingShingle | Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-021-89742-y |
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