ArticleDento maxillo facial radiology2022
Automatic detection of anteriorly displaced temporomandibular joint discs on magnetic resonance images using a deep learning algorithm.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.Dento maxillo facial radiology · 2025Pooled it
- CBCT-MRI-based prediction models for stratifying anterior disc displacement in orthodontic patients: development and independent internal validation of a retrospective diagnostic study.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
- Deep learning for automated detection and classification of temporomandibular joint disorders: A scoping review.Imaging science in dentistry · 2026Review
- 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
- The Application of Magnetic Resonance Imaging in Dentistry: A Bibliometric Analysis.International dental journal · 2026Review
- Development of AI-based tools for assessing temporomandibular joint disorders using MRI images.Bioinformation · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.Journal of dentistry · 2025Article
- Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.Dento maxillo facial radiology · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings.Medicina (Kaunas, Lithuania) · 2024Article
- Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.Frontiers in physiology · 2024Article
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.Dento maxillo facial radiology · 2023Article
- Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
- Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging.Scientific reports · 2022Article
- A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.International journal of molecular sciences · 2022Article
- Prevalence and characteristics of temporomandibular joint disc displacement based on Magnetic Resonance Imaging (MRI) in pre-clinical diagnosis: A retrospective study.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop models that can automatically detect anterior disc displacement (ADD) of the temporomandibular joint (TMJ) on MRIs before orthodontic treatment to reduce the risk of developing serious complications after treatment.
methodsWe used 9009 sagittal MRI of the TMJ as input and constructed three sets of deep learning models to detect ADD automatically. Deep learning models were developed using a convolutional neural network (CNN) based on the ResNet architecture and the "Imagenet" database. Five-fold cross-validation, oversampling, and data augmentation techniques were applied to reduce the risk of overfitting the model. The accuracy and area under the curve (AUC) of the three models were compared.
resultsThe performance of the maximum open mouth position model was excellent with accuracy and AUC of 0.970 (±0.007) and 0.990 (±0.005), respectively. For closed mouth position models, the accuracy and AUC of diagnostic Criteria 1 were 0.863 (±0.008) and 0.922 (±0.009), respectively significantly higher than that of diagnostic Criteria 2 with 0.839 (±0.013) (
conclusionOur CNN model resulted in high accuracy and AUC in detecting ADD and can therefore potentially be used by clinicians to assess ADD before orthodontic treatment, and hence improve treatment outcomes.
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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.