ArticleHealth science reports2025
The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning.Turkish journal of orthodontics · 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
- The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.Health science reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Dentofacial orthopedic treatment planning highly depends on the estimation of skeletal growth peak. In most cases, chronological age and biological age differ, some techniques estimate skeletal age by analyzing cervical vertebrae maturation (CVM) staging on lateral cephalograms. In this study, we aimed to review the different applications of AI in orthodontics and specifically discussed the different designs of AI models used for CVM estimation. Methods: Comprehensive searches was conducted across databases including PubMed, Web of Science, Google scholar, Embase, and Scopus using keywords such as orthodontics, cervical vertebra maturation, skeletal age and artificial intelligence. Results: Utilizing AI algorithms in assessing CVM-based skeletal age enhanced the accuracy of diagnosis, reduced analysis time and minimized observer variability. Deep learning techniques, especially convolutional neural networks (CNNs), have demonstrated promising results in identifying CVM stages from lateral cephalometric radiographs. Conclusion: AI algorithms can assist orthodontists in identifying CVM stages on lateral cephalograms. While the accuracy of AI algorithm depends on factors such as data set size, labeling methods, and model design, expert supervision and a solid understanding of CVM principles remain essential. AI should be considered as a supportive tool, not a replacement for clinical judgment.
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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 Socratic graph.