Restoration's Longevity in Endodontically Treated Teeth: A Machine Learning Survival Analysis From Randomised Clinical Trials
The models demonstrated high predictive performance, mainly in survival rate prediction. ML models show promise for developing a robust, data-driven framework to evaluate success and survival outcomes in ETT.
Authors
- Tatiana Pereira‐Cenci (ORCID: https://orcid.org/0000-0002-5166-8233)
- Jovito Adiel Skupien (ORCID: https://orcid.org/0000-0003-0892-3048)
- Luiz Alexandre Chisini (ORCID: https://orcid.org/0000-0002-3695-0361)
- Wietske Fokkinga (ORCID: https://orcid.org/0000-0001-9630-9800)
- Laylla Galdino dos Santos (ORCID: https://orcid.org/0000-0003-4396-0979)
- Bas Loomans (ORCID: https://orcid.org/0000-0002-6684-9723)
- Maximiliano Sérgio Cenci (ORCID: https://orcid.org/0000-0002-2543-6201)
- Fabiana Teixeira Marchiori
Institutions
- Radboud University Nijmegen (NL)
- Universidade Federal de Pelotas (BR)
- Radboud University Medical Center (NL)
- Franciscan University (BR)
Publication Details
- Journal
- International Endodontic Journal
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1111/iej.70048
- Citations
- 1
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.79
Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul