ArticleScientific reports2022
Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Comparing Traditional Versus AI-Assisted TMJ Disorder Management Approaches: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review.Dento maxillo facial radiology · 2025Pooled 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
- A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.Journal of imaging informatics in medicine · 2026Article
- Development of a Preliminary Diagnostic Tool for the Segmentation of Benign Jaw Lesions in CBCT Images Using nnU-Net v2: An Artificial Intelligence-Based Approach.Journal of imaging informatics in medicine · 2026Article
- Development of AI-based tools for assessing temporomandibular joint disorders using MRI images.Bioinformation · 2026Article
- An attention-based transfer learning model for diagnosing subluxation in temporomandibular joint panoramic radiographs.Scientific reports · 2025Article
- Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning.BMC oral health · 2025Article
- Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.Tomography (Ann Arbor, Mich.) · 2025Article
- Anterior Disc Displacement of Temporomandibular Joint: Imaging Diagnosis and Multidisciplinary Treatment.International journal of general medicine · 2025Review
- Automated tooth segmentation in magnetic resonance scans using deep learning - A pilot study.Dento maxillo facial radiology · 2025Article
- A numerical approach to bio-mechanics analysis based on live micro-CT images in the rat temporomandibular joint.Computational and structural biotechnology journal · 2025Article
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 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
- Review
- Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images.Dentistry journal · 2024Article
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.
Indexed as
Identifiers
What OpenQuestion holds
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.