ReviewJournal of clinical medicine2023
Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis.
Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 5 of them syntheses 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
15 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
- Temporomandibular joint arthritis in rheumatic diseases patients: which are the effective rehabilitative approaches for pain relief? A systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.Clinical and experimental dental research · 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
- Pooled it
- Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.Oral radiology · 2026Article
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Multimodal deep learning with anatomically constrained attention for screening MRI-detectable TMJ abnormalities from panoramic images.NPJ digital medicine · 2026Article
- Quo Vadis Temporomandibular Disorders? By 2050, the Global Prevalence of TMD May Approach 44.Journal of clinical medicine · 2025Article
- Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.Tomography (Ann Arbor, Mich.) · 2025Article
- Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data.Scientific reports · 2025Article
- An Algorithm for Jaw Pain among Divers.Journal of clinical medicine · 2024Article
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35-0.95) and the specificity was 0.79 (95% CI 0.75-0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.
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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.