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

Autor: Lucia H. S. Cevidanes, Reza Soroushmehr, Hongtu Zhu, Jonas Bianchi, Lawrence M Ashman, Beatriz Paniagua, Erika Benavides, William V. Giannobile, Fabiana N. Soki, Kayvan Najarian, Tengfei Li, Marilia Yatabe, Martin Styner, Antonio Carlos de Oliveira Ruellas, David H. Walker, João Roberto Gonçalves, Juan Carlos Prieto, James V. Sugai
Přispěvatelé: School of Dentistry, Universidade Estadual Paulista (Unesp), Inc., University of North Carolina, Center for Integrative Research in Critical Care and Michigan Institute for Data Science
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Data Analysis
Male
0301 basic medicine
Saliva
Databases
Factual
Psychological intervention
lcsh:Medicine
Osteoarthritis
computer.software_genre
Logistic regression
Machine Learning
0302 clinical medicine
Diagnosis
lcsh:Science
education.field_of_study
Multidisciplinary
Temporomandibular Joint Disorders
medicine.anatomical_structure
Area Under Curve
Three-dimensional imaging
Female
Chronic disability
Symptom Assessment
Headaches
medicine.symptom
Population
Orthodontics
Machine learning
Article
03 medical and health sciences
stomatognathic system
medicine
Humans
education
business.industry
lcsh:R
Reproducibility of Results
030206 dentistry
medicine.disease
Temporomandibular joint
Radiography
stomatognathic diseases
Early Diagnosis
030104 developmental biology
ROC Curve
Dentistry
lcsh:Q
Artificial intelligence
business
computer
Biomarkers
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/s41598-020-64942-0
Popis: Made available in DSpace on 2020-12-12T02:41:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-12-01 After 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. University of Michigan Department of Orthodontics and Pediatric Dentistry School of Dentistry São Paulo State University (UNESP) Department of Pediatric Dentistry School of Dentistry Kitware Inc. University of North Carolina Department of Psychiatry and Computer Science University of North Carolina Department of Biostatistics University of Michigan Department of Periodontics and Oral Medicine School of Dentistry University of Michigan Department of Oral and Maxillofacial Surgery and Hospital Dentistry School of Dentistry University of North Carolina Department of Orthodontics University of Michigan Center for Integrative Research in Critical Care and Michigan Institute for Data Science Department of Computational Medicine and Bioinformatics São Paulo State University (UNESP) Department of Pediatric Dentistry School of Dentistry
Databáze: OpenAIRE
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