ArticleDento maxillo facial radiology2022
Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.
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 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- 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
- Region-Specific Attention-Enhanced U-Net: Architectural Optimization for Small-Target and Weak-Boundary Segmentation in TMJ MRI.Journal of imaging informatics in medicine · 2026Article
- Multimodal deep learning with anatomically constrained attention for screening MRI-detectable TMJ abnormalities from panoramic images.NPJ digital medicine · 2026Article
- Automatic classification of temporomandibular joint disorders by magnetic resonance imaging and convolutional neural networks.Journal of dental sciences · 2025Article
- Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.Clinical and experimental dental research · 2024Review
- An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings.Medicina (Kaunas, Lithuania) · 2024Article
- Multi-class segmentation of temporomandibular joint using ensemble deep learning.Scientific reports · 2024Article
- Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.JCI insight · 2024Article
- Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.Frontiers in physiology · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls.Diagnostics (Basel, Switzerland) · 2023Article
- Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation.Scientific reports · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.International journal of dentistry · 2023Article
Corrections and comments
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Authors and funding
10 authors.
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Abstract
objectivesThe aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data.
methodsIn total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test).
resultsAlthough the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B.
conclusionThe segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.
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