Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology
The model exhibited potential as a reliable tool for clinical use, demonstrating accuracy in TMJ ultrasonographic analysis. This study underscores the ability of AI-driven segmentation and measurement algorithms to bridge existing gaps in ultrasonographic imaging and lays the foundation for broader clinical applications.
Authors
- Karolina Nurzyǹska (ORCID: https://orcid.org/0000-0001-5137-5732)
- Rafał Obuchowicz (ORCID: https://orcid.org/0000-0001-5883-5551)
- Michał Strzelecki (ORCID: https://orcid.org/0000-0001-9102-4929)
- Adam Piórkowski (ORCID: https://orcid.org/0000-0003-4773-5322)
- Julia Lasek (ORCID: https://orcid.org/0000-0003-2516-1823)
Institutions
- Silesian University of Technology (PL)
- Jagiellonian University (PL)
- Lodz University of Technology (PL)
- AGH University of Krakow (PL)
Publication Details
- Journal
- Tomography
- Published
- 2025-02-27
- DOI
- https://doi.org/10.3390/tomography11030027
- Citations
- 8
- Primary Topic
- Temporomandibular Joint Disorders
- Type
- article
- Field-Weighted Citation Impact
- 17.11