Beyond dental radiographs, a radiomics-based study for the classification of caries extension and depth
Radiomics enables caries depth classification from intraoral photographs, offering a non-invasive, cost-effective alternative to radiographs. This approach could revolutionize diagnostics by reducing reliance on invasive radiological techniques, using accessible and affordable equipment.
Authors
- Francesca Angelone (ORCID: https://orcid.org/0000-0001-8288-7087)
- Parisa Soltani (ORCID: https://orcid.org/0000-0002-9273-5279)
- Sandro Rengo (ORCID: https://orcid.org/0000-0002-4317-8031)
- Gianrico Spagnuolo (ORCID: https://orcid.org/0000-0003-3769-9786)
- Francesco Amato (ORCID: https://orcid.org/0000-0002-9053-3139)
- Mario Sansone (ORCID: https://orcid.org/0000-0003-3556-7771)
- Niccolò Giuseppe Armogida (ORCID: https://orcid.org/0000-0002-3259-3045)
- Alfonso Maria Ponsiglione (ORCID: https://orcid.org/0000-0003-1346-515X)
- Carlo Rengo (ORCID: https://orcid.org/0000-0003-1127-0877)
- Luigi Esposito
Institutions
- Kaohsiung Medical University (TW)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Journal of Dental Sciences
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1016/j.jds.2025.04.006
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Kaohsiung Medical University