ArticleBioMed research international2021
Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 5 of them syntheses that pooled it.
What it found
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Who cites it
25 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Comparing Traditional Versus AI-Assisted TMJ Disorder Management Approaches: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.Dento maxillo facial radiology · 2025Pooled it
- Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Pooled it
- MRI-Based Deep Learning Image Classification for Screening Temporomandibular Joint Degenerative Joint Disease.Journal of clinical medicine · 2026Article
- Quantitative Imaging of the Mandibular Condyle: Radiomics and Microarchitectural Biomarkers.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Automatic Prediction of TMJ Disc Displacement in CBCT Images Using Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning.BMC oral health · 2025Article
- Development and validation of an MRI radiomics-based interpretable machine learning model for predicting the progression-free survival in locally advanced nasopharyngeal carcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Development and validation of radiomics model for MRI-based identification of anterior talofibular ligament injuries.Scientific reports · 2025Article
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Quantitative MRI texture analysis of the lateral pterygoid muscle in unilateral temporomandibular joint disorders.Head & face medicine · 2025Article
- Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomic Parameters in Periapical Lesions: A CBCT Analysis Evaluating Volumetric Size, Cortical Expansion, Erosion, and Shape.European endodontic journal · 2024Article
- Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.Clinical and experimental dental research · 2024Review
- Automatic detection and visualization of temporomandibular joint effusion with deep neural network.Scientific reports · 2024Article
- Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.JCI insight · 2024Article
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma.Translational lung cancer research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A radiomics platform was used to extract (Huiying Medical Technology Co., Ltd., China) imaging features of TMJ pathologies, condylar bone changes, and disc displacements. Thereafter, different machine learning (ML) algorithms and logistic regression were implemented on radiomic features for feature selection, classification, and prediction. The following radiomic features included first-order statistics, shape, texture, gray-level cooccurrence matrix (GLCM), gray-level run length matrix (GLRLM), and gray-level size zone matrix (GLSZM). Six classifiers, including logistic regression (LR), random forest (RF), decision tree (DT),
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.