Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.

Autor: Orhan K; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey.; Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-059 Lublin, Poland.; Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06000, Turkey.; Department of Oral Diagnostics, Faculty of Dendistry, Semmelweis University, 1088 Budapest, Hungary., Yazici G; Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Gazi University, Ankara 06490, Turkey., Önder M; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey., Evli C; Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey., Volkan-Yazici M; Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Yuksek Ihtisas University, Ankara 06520, Turkey., Kolsuz ME; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey., Bağış N; Department of Periodontology, Faculty of Dentistry, Ankara University, Ankara 06560, Turkey., Kafa N; Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Gazi University, Ankara 06490, Turkey., Gönüldaş F; Department of Prosthetic Dentistry, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 May 31; Vol. 14 (11). Date of Electronic Publication: 2024 May 31.
DOI: 10.3390/diagnostics14111158
Abstrakt: Background and Objectives: We aimed to develop a predictive model for the outcome of bruxism treatments using ultrasonography (USG)-based machine learning (ML) techniques. This study is a quantitative research study (predictive modeling study) in which different treatment methods applied to bruxism patients are evaluated through artificial intelligence.
Materials and Methods: The study population comprised 102 participants with bruxism in three treatment groups: Manual therapy, Manual therapy and Kinesio Tape or Botulinum Toxin-A injection. USG imaging was performed on the masseter muscle to calculate muscle thickness, and pain thresholds were evaluated using an algometer. A radiomics platform was utilized to handle imaging and clinical data, as well as to perform a subsequent radiomics statistical analysis.
Results: The area under the curve (AUC) values of all machine learning methods ranged from 0.772 to 0.986 for the training data and from 0.394 to 0.848 for the test data. The Support Vector Machine (SVM) led to excellent discrimination between bruxism and normal patients from USG images. Radiomics characteristics in pre-treatment ultrasound scans of patients, showing coarse and nonuniform muscles, were associated with a greater chance of less effective pain reduction outcomes.
Conclusions: This study has introduced a machine learning model using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG. Support Vector Machine regression analysis showed the combined ML models can also predict the outcome of the pain reduction.
Databáze: MEDLINE
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