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Multi-class segmentation of temporomandibular joint using ensemble deep learning
DC Field | Value | Language |
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dc.contributor.author | 김재영 | - |
dc.contributor.author | 허종기 | - |
dc.date.accessioned | 2024-10-04T02:47:10Z | - |
dc.date.available | 2024-10-04T02:47:10Z | - |
dc.date.issued | 2024-08 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200597 | - |
dc.description.abstract | Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Image Processing, Computer-Assisted / methods | - |
dc.subject.MESH | Magnetic Resonance Imaging* / methods | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Mandibular Condyle / diagnostic imaging | - |
dc.subject.MESH | Mandibular Condyle / pathology | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Temporal Bone / diagnostic imaging | - |
dc.subject.MESH | Temporomandibular Joint Disorders* / diagnostic imaging | - |
dc.subject.MESH | Temporomandibular Joint* / diagnostic imaging | - |
dc.subject.MESH | Temporomandibular Joint* / pathology | - |
dc.title | Multi-class segmentation of temporomandibular joint using ensemble deep learning | - |
dc.type | Article | - |
dc.contributor.college | College of Dentistry (치과대학) | - |
dc.contributor.department | Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) | - |
dc.contributor.googleauthor | Kyubaek Yoon | - |
dc.contributor.googleauthor | Jae-Young Kim | - |
dc.contributor.googleauthor | Sun-Jong Kim | - |
dc.contributor.googleauthor | Jong-Ki Huh | - |
dc.contributor.googleauthor | Jin-Woo Kim | - |
dc.contributor.googleauthor | Jongeun Choi | - |
dc.identifier.doi | 39160234 | - |
dc.contributor.localId | A00861 | - |
dc.contributor.localId | A04365 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 10.1038/s41598-024-69814-5 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Segmentation | - |
dc.subject.keyword | Temporomandibular joint | - |
dc.contributor.alternativeName | Kim, Jae Young | - |
dc.contributor.affiliatedAuthor | 김재영 | - |
dc.contributor.affiliatedAuthor | 허종기 | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 18990 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.14(1) : 18990, 2024-08 | - |
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