Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorSchool of Dentistry-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorInc.-
Autor(es): dc.contributorUniversity of North Carolina-
Autor(es): dc.contributorCenter for Integrative Research in Critical Care and Michigan Institute for Data Science-
Autor(es): dc.creatorBianchi, Jonas [UNESP]-
Autor(es): dc.creatorde Oliveira Ruellas, Antônio Carlos-
Autor(es): dc.creatorGonçalves, João Roberto [UNESP]-
Autor(es): dc.creatorPaniagua, Beatriz-
Autor(es): dc.creatorPrieto, Juan Carlos-
Autor(es): dc.creatorStyner, Martin-
Autor(es): dc.creatorLi, Tengfei-
Autor(es): dc.creatorZhu, Hongtu-
Autor(es): dc.creatorSugai, James-
Autor(es): dc.creatorGiannobile, William-
Autor(es): dc.creatorBenavides, Erika-
Autor(es): dc.creatorSoki, Fabiana-
Autor(es): dc.creatorYatabe, Marilia-
Autor(es): dc.creatorAshman, Lawrence-
Autor(es): dc.creatorWalker, David-
Autor(es): dc.creatorSoroushmehr, Reza-
Autor(es): dc.creatorNajarian, Kayvan-
Autor(es): dc.creatorCevidanes, Lucia Helena Soares-
Data de aceite: dc.date.accessioned2022-02-22T00:34:27Z-
Data de disponibilização: dc.date.available2022-02-22T00:34:27Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-020-64942-0-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201760-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201760-
Descrição: dc.descriptionAfter chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.-
Descrição: dc.descriptionUniversity of Michigan Department of Orthodontics and Pediatric Dentistry School of Dentistry-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Pediatric Dentistry School of Dentistry-
Descrição: dc.descriptionKitware Inc.-
Descrição: dc.descriptionUniversity of North Carolina Department of Psychiatry and Computer Science-
Descrição: dc.descriptionUniversity of North Carolina Department of Biostatistics-
Descrição: dc.descriptionUniversity of Michigan Department of Periodontics and Oral Medicine School of Dentistry-
Descrição: dc.descriptionUniversity of Michigan Department of Oral and Maxillofacial Surgery and Hospital Dentistry School of Dentistry-
Descrição: dc.descriptionUniversity of North Carolina Department of Orthodontics-
Descrição: dc.descriptionUniversity of Michigan Center for Integrative Research in Critical Care and Michigan Institute for Data Science Department of Computational Medicine and Bioinformatics-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Pediatric Dentistry School of Dentistry-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleOsteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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