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Background <p>Accurate diagnosis of anterior disc displacement (ADD) is essential for managing temporomandibular joint disorders (TMJ). This study employed machine learning (ML) to automatically detect anteriorly displaced TMJ discs in magnetic resonance images (MRI).</p> Methods <p>This retrospective study included patients with TMJ disorders who visited the Hospital between January 2023 and June 2024. Five machine learning models—decision tree (DT), K-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR)—were utilized to train and validate radiomics data derived from TMJ imaging. Model performance was assessed using an 8:2 train-test split, evaluating accuracy with metrics such as area under the curve (AUC), sensitivity, specificity, precision, and F1 score. After manual delineation of TMJ ROIs by an experienced radiologist (serving as reference standard), radiomic feature extraction included first-order statistics, size- and shape-based features, and texture features.The open-phase, close-phase, and open and close fusion radiomics image features were evaluated separately.</p> Results <p>The study analyzed 382 TMJs from 191 patients, comprising 214 normal joints and 168 abnormal joints. The fusion radiomics model using five classifiers surpassed both open-phase and close-phase models, demonstrating superior performance in both training and validation cohorts. The fusion radiomics model consistently outperformed single-phase analyses across both diagnostic tasks. For normal vs. abnormal TMJ discrimination, the Random Forest (RF) classifier demonstrated robust performance with AUCs of 0.889 (95% CI: 0.854–0.924) in training and 0.874 (95% CI: 0.799–0.948) in validation.Complete performance metrics for all five classifiers are detailed in the main text.</p> Conclusions <p>The fusion radiomics model effectively distinguished normal from abnormal joints and differentiated between ADDwR and ADDwoR, supporting personalized treatment planning.</p> Clinical trial number <p>not applicable.</p>

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Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Bin Ji,
  • Yang Liu,
  • Bin Zhou,
  • Rui Mi,
  • Yumeng Liu,
  • Yungang Lv,
  • Panying Wang,
  • Yanjiao Li,
  • Qingjun Sun,
  • Nashan Wu,
  • Yuping Quan,
  • Songxiong Wu,
  • Long Yan

摘要

Background

Accurate diagnosis of anterior disc displacement (ADD) is essential for managing temporomandibular joint disorders (TMJ). This study employed machine learning (ML) to automatically detect anteriorly displaced TMJ discs in magnetic resonance images (MRI).

Methods

This retrospective study included patients with TMJ disorders who visited the Hospital between January 2023 and June 2024. Five machine learning models—decision tree (DT), K-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR)—were utilized to train and validate radiomics data derived from TMJ imaging. Model performance was assessed using an 8:2 train-test split, evaluating accuracy with metrics such as area under the curve (AUC), sensitivity, specificity, precision, and F1 score. After manual delineation of TMJ ROIs by an experienced radiologist (serving as reference standard), radiomic feature extraction included first-order statistics, size- and shape-based features, and texture features.The open-phase, close-phase, and open and close fusion radiomics image features were evaluated separately.

Results

The study analyzed 382 TMJs from 191 patients, comprising 214 normal joints and 168 abnormal joints. The fusion radiomics model using five classifiers surpassed both open-phase and close-phase models, demonstrating superior performance in both training and validation cohorts. The fusion radiomics model consistently outperformed single-phase analyses across both diagnostic tasks. For normal vs. abnormal TMJ discrimination, the Random Forest (RF) classifier demonstrated robust performance with AUCs of 0.889 (95% CI: 0.854–0.924) in training and 0.874 (95% CI: 0.799–0.948) in validation.Complete performance metrics for all five classifiers are detailed in the main text.

Conclusions

The fusion radiomics model effectively distinguished normal from abnormal joints and differentiated between ADDwR and ADDwoR, supporting personalized treatment planning.

Clinical trial number

not applicable.