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BMC Oral Health

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Open Access 01-12-2023 | Research

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

Authors: Teodora Popova, Thomas Stocker, Yeganeh Khazaei, Yoana Malenova, Andrea Wichelhaus, Hisham Sabbagh

Published in: BMC Oral Health | Issue 1/2023

Abstract

Background

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.

Methods

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 < 0.05.

Results

The 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.

Conclusions

The 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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Title: Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network
Authors: Teodora Popova
Thomas Stocker
Yeganeh Khazaei
Yoana Malenova
Andrea Wichelhaus
Hisham Sabbagh
Publication date: 01-12-2023
Publisher: BioMed Central
Published in: BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI: https://doi.org/10.1186/s12903-023-02984-2

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Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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