ArticleScientific reports2023
An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 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
- Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.Clinical and experimental dental research · 2025Pooled it
- Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.Oral radiology · 2026Article
- Can Deep Learning Methods Differentiate Temporomandibular Joint Disorders From Healthy Joints? A 3D Artificial Intelligence Algorithm Study Based on CBCT Images.Journal of oral rehabilitation · 2026Article
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Deep learning for automated detection and classification of temporomandibular joint disorders: A scoping review.Imaging science in dentistry · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning.Progress in orthodontics · 2026Article
- Application of artificial intelligence in the diagnosis and management of temporomandibular joint osteoarthritis using cone-beam computed tomography: An evidence-based systematic review.Imaging science in dentistry · 2025Review
- Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Artificial intelligence in the diagnosis of temporomandibular joint disorders using cone-beam computed tomography (CBCT).Bioinformation · 2025Article
- An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings.Medicina (Kaunas, Lithuania) · 2024Article
- Tomographic assessment of glenoid fossa morphology and position in symmetric and asymmetric patients.Science progressObservational
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7 authors.
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Abstract
The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen's kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.
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