ArticleBMC oral health2023
Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial intelligence in healthcare, dentistry, and orthodontics; is it an opportunity or a threat? An epistemological review.Philosophy, ethics, and humanities in medicine : PEHM · 2026Review
- An Open-Source, AI-Supported Teaching Tool in Orthodontic Education-Assessment of Acceptance and Effectiveness.Journal of dental education · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks?BMC medical imaging · 2024Article
- Comparative evaluation of commercially available AI-based cephalometric tracing programs.BMC oral health · 2024Article
- Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks.
methodsFor the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05.
resultsThe study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances.
conclusionsThe results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
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