Machine learning‐assisted prediction of clinical responses to periodontal treatment
Using comprehensive data from patients with periodontitis, an inflammatory condition of the tooth-supporting tissues, a machine learning model was trained to predict how well patients might respond to different treatments after 1 year. The model was externally tested in patient populations from 2 continents different from the training dataset. The results suggest that with further research and refinement, this tool could eventually become a valuable asset in personalizing treatment plans for improved patient outcomes.
Authors
- Poliana Mendes Duarte (ORCID: https://orcid.org/0000-0001-8872-5943)
- Andreas A. Werdich (ORCID: https://orcid.org/0000-0002-1222-1180)
- Eduardo Henrique de Souza OLIVEIRA (ORCID: https://orcid.org/0000-0001-5823-6591)
- Magda Feres (ORCID: https://orcid.org/0000-0002-2293-3392)
- William V. Giannobile (ORCID: https://orcid.org/0000-0002-7102-9746)
- Balázs Fehér (ORCID: https://orcid.org/0000-0003-4386-6237)
Institutions
- World Intellectual Property Organization (CH)
- International Telecommunication Union (CH)
- Harvard University (US)
- University of Florida (US)
- World Health Organization (CH)
- Boston VA Research Institute (US)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Journal of Periodontology
- Published
- 2025-04-20
- DOI
- https://doi.org/10.1002/jper.24-0737
- Citations
- 8
- Primary Topic
- Oral microbiology and periodontitis research
- Type
- article
- Field-Weighted Citation Impact
- 11.72
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Osteology Foundation
- National Institutes of Health
- Fogarty International Center