Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network

One 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 landma...

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Bibliographic Details
Published in:BMC oral health 2023-05, Vol.23 (1), p.274-10, Article 274
Main Authors: Popova, Teodora, Stocker, Thomas, Khazaei, Yeganeh, Malenova, Yoana, Wichelhaus, Andrea, Sabbagh, Hisham
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Artificial Intelligence
Automation
Cephalometric landmarks
Cephalometry
Cephalometry - methods
Convolutional neural network
Datasets
Deep learning
Dentition
Developmental stages
Health aspects
Humans
Morphology
Neural networks
Neural Networks, Computer
Orthodontic Appliances
Orthodontic Brackets
Orthodontics
Patients
Radiography
Services
Software
Teeth
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Summary:One 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. For 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 
ISSN:1472-6831
1472-6831
DOI:10.1186/s12903-023-02984-2