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

ABSTRACT:

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,

SUBMITTER: Ji B 

PROVIDER: S-EPMC12513056 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature