ArticleBMC oral health2023
Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Artificial intelligence for craniofacial and mandibular growth prediction: A systematic review.Clinical oral investigations · 2026Pooled it
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- Machine Learning-Based Model for Predicting Short- and Long-Term Growth in Untreated Class III Malocclusion.Orthodontics & craniofacial research · 2026Article
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence Models for the Detection and Quantification of Orthodontically Induced Root Resorption Using Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis.Dentistry journal · 2026Review
- Deep learning for Angle classification based on intraoral photographs: an interpretability perspective.BMC oral health · 2026Article
- Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.BMC oral health · 2025Article
- The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.Health science reports · 2025Article
- Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.BMC oral health · 2025Article
- Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments-a narrative review.Frontiers in dental medicine · 2025Review
- 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 Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network.Journal of imaging informatics in medicine · 2024Article
- Validation of Machine Learning Models for Craniofacial Growth Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Finite element analysis of sagittal screw expander appliance in the treatment of anterior maxillary hypoplasia.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundIt is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into normal or overdeveloped using cephalometric radiographs.
methodsA deep convolutional neural network (CNN) model was constructed based on the algorithm ResNet50 and trained on the basis of 256 cephalometric radiographs. The prediction behavior of the model was tested on 40 cephalograms and visualized by equipped with Grad-CAM. The prediction performance of the CNN model was compared with that of three junior orthodontists.
resultsThe deep-learning model showed a good prediction accuracy about 85%, much higher when compared with the 54.2% of the junior orthodontists. The sensitivity and specificity of the model was 0.95 and 0.75 respectively, higher than that of the junior orthodontists (0.62 and 0.47 respectively). The area under the curve value of the deep-learning model was 0.9775. Visual inspection showed that the model mainly focused on the characteristics of special regions including chin, lower edge of the mandible, incisor teeth, airway and condyle to conduct the prediction.
conclusionsThe deep-learning CNN model could predict the growth trend of the mandible in anterior crossbite children with relatively high accuracy using cephalometric images. The deep learning model made the prediction decision mainly by identifying the characteristics of the regions of chin, lower edge of the mandible, incisor teeth area, airway and condyle in cephalometric images.
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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.