Toward artificial intelligence in dental prosthesis planning — a preliminary in-silico feasibility study
The ability of ANNs to learn dental prosthesis planning was confirmed within the limitations of this preliminary in-silico study. The findings of this study corroborate that ANNs have the potential to support clinicians by providing automated recommendations for choices of dental prosthesis consistent with relevant rules, ultimately supporting and enhancing clinicians' decision making. Moreover, such ANNs may, in principle, enable advanced patient self-assessment of treatment needs and improve patient care in prosthodontics.
Authors
- Philipp del Hougne (ORCID: https://orcid.org/0000-0002-4821-3924)
- Michael del Hougne (ORCID: https://orcid.org/0000-0001-5563-7445)
- Christian Höhne (ORCID: https://orcid.org/0000-0001-5603-5577)
- Isabella Di Lorenzo (ORCID: https://orcid.org/0009-0001-9859-8017)
- Marc Schmitter
- Johannes Schrenker
Institutions
- Centre National de la Recherche Scientifique (FR)
- University of Würzburg (DE)
- Institut d'Électronique et des Technologies du numéRique (FR)
- Université de Rennes (FR)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-08-31
- DOI
- https://doi.org/10.1186/s12903-025-06778-6
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65