Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data
Abstract This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 i...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83750-4 |
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| author | Eunhye Choi Seokwon Shin Kijin Lee Taejin An Richard K. Lee Sunmin Kim Youngdoo Son Seong Teak Kim |
| author_facet | Eunhye Choi Seokwon Shin Kijin Lee Taejin An Richard K. Lee Sunmin Kim Youngdoo Son Seong Teak Kim |
| author_sort | Eunhye Choi |
| collection | DOAJ |
| description | Abstract This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI’s capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods. |
| format | Article |
| id | doaj-art-68cb3a9720cf40c991d475c0b0d4a0e8 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-68cb3a9720cf40c991d475c0b0d4a0e82025-08-20T02:24:29ZengNature PortfolioScientific Reports2045-23222025-01-011511710.1038/s41598-024-83750-4Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise dataEunhye Choi0Seokwon Shin1Kijin Lee2Taejin An3Richard K. Lee4Sunmin Kim5Youngdoo Son6Seong Teak Kim7School of Dentistry, Dental Research Institute, Seoul National UniversityDepartment of Industrial and Systems Engineering, Dongguk UniversityDepartment of Industrial and Systems Engineering, Dongguk UniversityDepartment of Industrial and Systems Engineering, Dongguk UniversitySchool of Biomedical Engineering, University of Technology SydneyCollege of Arts and Sciences, Boston UniversityDepartment of Industrial and Systems Engineering, Dongguk UniversityDepartment of Orofacial Pain and Oral Medicine, Yonsei University College of DentistryAbstract This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI’s capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods.https://doi.org/10.1038/s41598-024-83750-4Temporomandibular jointDegenerative joint diseaseArtificial intelligenceTemporomandibular joint panoramic radiographyJoint noise |
| spellingShingle | Eunhye Choi Seokwon Shin Kijin Lee Taejin An Richard K. Lee Sunmin Kim Youngdoo Son Seong Teak Kim Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data Scientific Reports Temporomandibular joint Degenerative joint disease Artificial intelligence Temporomandibular joint panoramic radiography Joint noise |
| title | Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| title_full | Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| title_fullStr | Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| title_full_unstemmed | Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| title_short | Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| title_sort | artificial intelligence enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data |
| topic | Temporomandibular joint Degenerative joint disease Artificial intelligence Temporomandibular joint panoramic radiography Joint noise |
| url | https://doi.org/10.1038/s41598-024-83750-4 |
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