A personalized periodontitis risk based on nonimage electronic dental records by machine learning
If such ML models become clinically applied, clinicians can be assisted with personalized risk predictions based on the individual. If the key riskcontributing factors for the individual are revealed/provided, ML can suggest targeted prevention interventions. These advancements can contribute to a reduced workload, sustainable EDRs, data-based dental care, and, ultimately, improved patient outcomes.
Authors
- Bruno G. Loos (ORCID: https://orcid.org/0000-0002-8794-552X)
- Reuben Applegate
- Laura Swinckels (ORCID: https://orcid.org/0000-0003-4096-5491)
- Harmen Bijwaard (ORCID: https://orcid.org/0000-0003-1314-5260)
- Krishna Kumar Kookal (ORCID: https://orcid.org/0000-0002-8228-5796)
- Elsbeth Kalenderian (ORCID: https://orcid.org/0000-0002-4684-9793)
- Josef J. M. Bruers (ORCID: https://orcid.org/0000-0002-3755-2947)
- de A Keijzer
Institutions
- Marquette University (US)
- Avans University of Applied Sciences (NL)
- Academic Center for Dentistry Amsterdam (NL)
- National Institute for Public Health and the Environment (NL)
- University of Pretoria (ZA)
- Vrije Universiteit Amsterdam (NL)
- University of Amsterdam (NL)
- Inholland University of Applied Sciences (NL)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1016/j.jdent.2024.105469
- Citations
- 10
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.93