Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Michigan-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorArthur A. Dugoni School of Dentistry-
Autor(es): dc.contributorUniversity of North Carolina-
Autor(es): dc.creatorZhang, Winston-
Autor(es): dc.creatorBianchi, Jonas-
Autor(es): dc.creatorTurkestani, Najla Al-
Autor(es): dc.creatorLe, Celia-
Autor(es): dc.creatorDeleat-Besson, Romain-
Autor(es): dc.creatorRuellas, Antonio-
Autor(es): dc.creatorCevidanes, Lucia-
Autor(es): dc.creatorYatabe, Marilia-
Autor(es): dc.creatorGoncalves, Joao-
Autor(es): dc.creatorBenavides, Erika-
Autor(es): dc.creatorSoki, Fabiana-
Autor(es): dc.creatorPrieto, Juan-
Autor(es): dc.creatorPaniagua, Beatriz-
Autor(es): dc.creatorNajarian, Kayvan-
Autor(es): dc.creatorGryak, Jonathan-
Autor(es): dc.creatorSoroushmehr, Reza-
Data de aceite: dc.date.accessioned2025-08-21T21:53:44Z-
Data de disponibilização: dc.date.available2025-08-21T21:53:44Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/EMBC46164.2021.9629990-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/223211-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/223211-
Descrição: dc.descriptionDiagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.-
Descrição: dc.descriptionDepartment of Computational Medicine and Bioinformatics University of Michigan-
Descrição: dc.descriptionDepartment of Orthodontics and Pediatric Dentistry University of Michigan-
Descrição: dc.descriptionPediatric Dentistry and Orthodontics Sao Paulo State University-
Descrição: dc.descriptionDepartment of Orthodontics University of the Pacific Arthur A. Dugoni School of Dentistry-
Descrição: dc.descriptionDepartment of Periodontics and Oral Medicine University of Michigan-
Descrição: dc.descriptionUniversity of North Carolina-
Descrição: dc.descriptionDepartments of Psychiatry Orthodontics and Computer Science University of North Carolina-
Descrição: dc.descriptionPediatric Dentistry and Orthodontics Sao Paulo State University-
Formato: dc.format1810-1813-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS-
???dc.source???: dc.sourceScopus-
Título: dc.titleTemporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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