Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands
The prediction model effectively differentiated Sjögren's syndrome using radiomics and machine learning. Use of Prediction One significantly streamlined the workflow, including analysis of radiomics, creation of the prediction model, and evaluation of performance, while substantially reducing the time required.
Authors
- Eiichiro Ariji (ORCID: https://orcid.org/0000-0002-5245-5395)
- Motoki Fukuda (ORCID: https://orcid.org/0000-0002-0285-5008)
- T Shibata
- Yoshitaka Kise (ORCID: https://orcid.org/0000-0002-5890-2950)
- Takuma Funakoshi (ORCID: https://orcid.org/0000-0002-5423-8968)
- Yoshiko Ariji (ORCID: https://orcid.org/0000-0002-7079-0867)
Institutions
- Aichi Gakuin University (JP)
- Osaka Dental University (JP)
Publication Details
- Journal
- Imaging Science in Dentistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5624/isd.20250022
- Citations
- 1
- Primary Topic
- Salivary Gland Disorders and Functions
- Type
- article
- Field-Weighted Citation Impact
- 1.16