ArticleOral radiology2023
Deep convolutional neural network-the evaluation of cervical vertebrae maturation.
Article in Oral radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 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
- Problem Formulation Outweighs Loss-Function Choice in Deep-Learning Cervical Vertebral Maturation Staging: A Multi-Seed Study on an Imbalanced Public Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Performance evaluation of deep learning models for overbite classification on cephalometric radiographs.European oral research · 2026Article
- 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
- Deep learning based quantitative cervical vertebral maturation analysis.Head & face medicine · 2025Article
- Deep learning approaches for quantitative and qualitative assessment of cervical vertebral maturation staging systems.PloS one · 2025Article
- Convolutional Neural Network-Based Deep Learning Methods for Skeletal Growth Prediction in Dental Patients.Journal of imaging · 2024Article
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
- Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.Sensors (Basel, Switzerland) · 2024Article
- Mapping an intelligent algorithm for predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy.Progress in orthodontics · 2024Article
- Determination of cervical vertebral maturation using machine learning in lateral cephalograms.Journal of dental research, dental clinics, dental prospects · 2024Article
- Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score.
methodsA total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries.
resultsAs a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2.
conclusionThe experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification.
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Registered trials
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