SynthesisDento maxillo facial radiology2025
Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.
Synthesis in Dento maxillo facial radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of artificial intelligence models for temporomandibular joint anomalies on MRI: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Quantifying symmetry in mandibular condyle motion: a real-time MRI approach.Journal of oral & facial pain and headache · 2025Trial
- Review
- Artificial Intelligence for Diagnosis of Temporomandibular and Cranio-Cervico-Mandibular Musculoskeletal Disorders: A Systematic Review and Exploratory Diagnostic Test Accuracy Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Temporomandibular disorders in children and adolescents: an underexplored condition in need of developmentally informed diagnostic guidance.Pain management · 2026Review
- Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.Journal of oral & facial pain and headache · 2026Review
- Multimodal deep learning with anatomically constrained attention for screening MRI-detectable TMJ abnormalities from panoramic images.NPJ digital medicine · 2026Article
- Correlation between signal intensity ratio of magnetic resonance imaging and arthroscopic findings in temporomandibular joint synovitis.Journal of dental sciences · 2026Article
- Global research trends in MRI of temporomandibular disorders: a bibliometric study and visualization analysis via CiteSpace.Journal of oral & facial pain and headache · 2025Article
- Digital Dental Radiology and Diagnostics-From 2D to 3D.Australian dental journal · 2025Review
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Quo Vadis Temporomandibular Disorders? By 2050, the Global Prevalence of TMD May Approach 44.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectivesTo summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.
methodsStudies were gathered by searching 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.
resultsThirteen studies were included in this systematic review. Most of the studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN-based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.
conclusionsThe integration of AI, particularly deep learning, in TMJ MRI, shows promising results as a diagnostic-assistance tool to segment TMJ structures and classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.
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