ArticleDento maxillo facial radiology2020
Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models.
Article in Dento maxillo facial radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.Oral radiology · 2026Pooled it
- Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Neural networks for classification of cervical vertebrae maturation: a systematic review.The Angle orthodontist · 2022Pooled it
- Diagnostic Accuracy of a Machine Learning Model for Cervical Vertebra-Based Skeletal Maturity Assessment in Pediatric and Adolescent Patients Using Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.Oral radiology · 2026Article
- Age Estimation of the Cervical Vertebrae Region Using Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Assessment of Cervical Vertebral Maturation and Chronological Age in Yemeni Children and Adolescents Using Lateral Cephalometric Radiographs.Medical science monitor : international medical journal of experimental and clinical research · 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
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Feasibility Study on Optimising the Efficacy of a Population Age Estimation Model for South China by Combined Machine Learning for the Second and Third Molars.Journal of imaging informatics in medicine · 2025Article
- Objective Assessment of Bone Age in Orthodontics: A Scoping Review.Journal of pharmacy & bioallied sciences · 2025Review
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.Imaging science in dentistry · 2025Review
- Expert consensus on imaging diagnosis and analysis of early correction of childhood malocclusion.International journal of oral science · 2025Review
- An explainable machine learning estimated biological age based on morphological parameters of the spine.GeroScience · 2025Article
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.Journal of dental sciences · 2025Article
- Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement.Progress in orthodontics · 2024Article
- Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages.BMC musculoskeletal disorders · 2024Article
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results.
methodsA total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results.
resultsAmong the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes.
conclusionsThis study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.
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