ArticleJournal of clinical medicine2021
Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 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
- Artificial intelligence for predicting the pubertal growth spurt using cephalometric and hand-wrist radiographs: a systematic review and meta-analysis.BMC oral health · 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
- 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
- Single-model deep learning approach for simultaneous cervical vertebral maturation staging and skeletal jaw relationship on lateral cephalograms using YOLOv8 and CNN.BMC oral health · 2026Article
- Assessing circum-maxillary sutures maturation by CBCT: a diagnostic indicator for skeletal age and treatment timing for class III malocclusion.Frontiers in oral health · 2026Article
- Age Estimation of the Cervical Vertebrae Region Using Deep Learning.Bioengineering (Basel, Switzerland) · 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
- Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach.Imaging science in dentistry · 2025Article
- Deep Learning Applications in Dental Image-Based Diagnostics: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- 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
- Deep learning based quantitative cervical vertebral maturation analysis.Head & face medicine · 2025Article
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning approaches for quantitative and qualitative assessment of cervical vertebral maturation staging systems.PloS one · 2025Article
- Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement.Progress in orthodontics · 2024Article
- Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.Journal of clinical medicine · 2024Review
- Transforming Adolescent Smiles: Correcting Skeletal Mandibular Retrusion and Bimaxillary Protrusion with Clear Aligners.Current medical science · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand-wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2-C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.
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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.