ArticleJournal of personalized medicine2022
The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method.International journal of legal medicine · 2026Article
- Dental age estimation: a scoping review comparing the manual application of the Demirjian method and artificial intelligence modalities.International journal of legal medicine · 2026Article
- Clinical biochemical evaluation of trace elements and insulin-like growth factor-I in children with short stature: Diagnostic and correlative insights from the Guangzhou cohort.Journal of medical biochemistry · 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
- Dental Age Estimation by Demirjian, Willems, and Cameriere Methods in Children with Familial Mediterranean Fever: A Case-Control Study.Children (Basel, Switzerland) · 2025Article
- U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm.BMC oral health · 2024Article
- Review
- Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study.Diagnostics (Basel, Switzerland) · 2023Article
- A population-based study to assess two convolutional neural networks for dental age estimation.BMC oral health · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThis study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children.
methodsThe panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample
resultsThe AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys.
conclusionThe AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.
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