Comparison of deep learning models to detect crossbites on 2D intraoral photographs
Convolutional neural networks show high potential in processing clinical photographs and detecting crossbites. This study provides initial insights into how deep learning models can be used for orthodontic diagnosis of malocclusions based on intraoral 2D photographs.
Authors
- Mohammedreza Sefidroodi (ORCID: https://orcid.org/0009-0008-6658-0964)
- Stratos Vassis (ORCID: https://orcid.org/0009-0006-3785-1011)
- Ruben Pauwels (ORCID: https://orcid.org/0000-0002-9462-7546)
- Beatrice Noeldeke (ORCID: https://orcid.org/0000-0003-2945-2396)
- Peter Bangsgaard Stoustrup (ORCID: https://orcid.org/0000-0002-0903-6718)
Institutions
- Chulalongkorn University (TH)
- Aarhus University (DK)
- Leibniz University of Applied Sciences (DE)
Publication Details
- Journal
- Head & Face Medicine
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1186/s13005-024-00448-8
- Citations
- 8
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.95