Orhan Kaan, Yazici Gokhan, Önder Merve, Evli Cengiz, Volkan-Yazici Melek, Kolsuz Mehmet Eray, Bağış Nilsun, Kafa Nihan, Gönüldaş Fehmi
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.
Diagnostics (Basel). 2024 May 31;14(11):1158. doi: 10.3390/diagnostics14111158.
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.
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.
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.
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.
我们旨在利用基于超声检查（USG）的机器学习（ML）技术开发一种磨牙症治疗结果的预测模型。本研究是一项定量研究（预测建模研究），通过人工智能对应用于磨牙症患者的不同治疗方法进行评估。
研究人群包括102名磨牙症患者，分为三个治疗组：手法治疗、手法治疗联合肌内效贴或肉毒杆菌毒素A注射。对咬肌进行USG成像以计算肌肉厚度，并使用痛觉计评估疼痛阈值。利用一个放射组学平台处理成像和临床数据，并进行后续的放射组学统计分析。
所有机器学习方法在训练数据中的曲线下面积（AUC）值范围为0.772至0.986，在测试数据中的范围为0.394至0.848。支持向量机（SVM）能够很好地区分磨牙症患者和正常患者的USG图像。患者治疗前超声扫描中显示肌肉粗糙且不均匀的放射组学特征与疼痛减轻效果较差的可能性更大相关。
本研究为磨牙症患者引入了一种基于超声（USG）图像的支持向量机分析机器学习模型，该模型可检测USG上咬肌的变化。支持向量机回归分析表明，联合机器学习模型也可以预测疼痛减轻的结果。
