ArticleScientific reports2021
Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- 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
- CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning.Progress in orthodontics · 2026Article
- Reliability of a Method to Quantify the Cortical Thickness of the Posterolateral Vertebral Arch on MRI.Indian journal of orthopaedics · 2026Article
- Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm.BMC oral health · 2025Article
- Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.BMC oral health · 2025Article
- Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.Oral 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
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs.BMC oral health · 2024Article
- Comparison of cone beam computed tomography and plane radiographs of radial fractures as a basis for radiographical measurements.BMC medical imaging · 2023Article
- A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Development of deep learning model and evaluation in real clinical practice of lingual mandibular bone depression (Stafne cyst) on panoramic radiographs.Dento maxillo facial radiology · 2023Article
- 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
- Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.Scientific reports · 2023Article
- From bench to bedside - current clinical and translational challenges in fibula free flap reconstruction.Frontiers in medicine · 2023Review
- Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.Dento maxillo facial radiology · 2023Review
- Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- Fully automated condyle segmentation using 3D convolutional neural networks.Scientific reports · 2022Article
- Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.Scientific reports · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 normal subjects, manually labeled by an oral radiologist, served as the gold-standard. The segmentation model combined a modified U-Net and a convolutional neural network for target region classification. Model performance was evaluated using intersection over union (IoU) and the Hausdorff distance in comparison with the gold standard. The second automated model measured the cortical thickness based on a three-dimensional (3D) model rendered from the segmentation results and presented a color visualization of the measurements. The IoU and Hausdorff distance showed high accuracy (0.870 and 0.928 for marrow bone and 0.734 and 1.247 for cortical bone, respectively). A visual comparison of the 3D color maps showed a similar trend to the gold standard. This algorithm for automatic segmentation of the mandibular condyle head and visualization of the measured cortical thickness as a 3D-rendered model with a color map may contribute to the automated quantification of bone thickness changes of the temporomandibular joint complex on CBCT.
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