Toward artificial intelligence in dental prosthesis planning — a preliminary in-silico feasibility study
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| Publicado en: | BMC Oral Health vol. 25 (2025), p. 1-11 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3247129133 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1472-6831 | ||
| 024 | 7 | |a 10.1186/s12903-025-06778-6 |2 doi | |
| 035 | |a 3247129133 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58473 |2 nlm | ||
| 100 | 1 | |a Michael del Hougne | |
| 245 | 1 | |a Toward artificial intelligence in dental prosthesis planning — a preliminary in-silico feasibility study | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundDental prosthesis planning is a multi-faceted and nuanced process of conceiving individual treatment plans based on dental findings and in line with established treatment guidelines. The aim of this study was to assess whether an artificial neural network (ANN) provided with sufficient training data could approximate this process.MethodsDental prosthesis planning was abstracted as a mapping from dental findings to choices of dental prosthesis. The problem was framed as a multi-output multi-class classification. An ANN was trained via supervised learning to approximate dental prosthesis planning based on synthetic datasets of dental findings and corresponding prosthesis choices. The accuracy on unseen test data was examined as a function of the ANN’s random initializations, the training set sizes, and the ANN architecture.ResultsWithin the scope and limitations of this study, the ANN achieved an accuracy of 99.51% (± 0.15).ConclusionsThe 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. | |
| 610 | 4 | |a Python Software Foundation | |
| 653 | |a Prosthodontics | ||
| 653 | |a Software | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Prostheses | ||
| 653 | |a Planning | ||
| 653 | |a Neural networks | ||
| 653 | |a Neurosciences | ||
| 653 | |a Dentures | ||
| 653 | |a Automation | ||
| 653 | |a Feasibility studies | ||
| 653 | |a Python | ||
| 653 | |a Teeth | ||
| 653 | |a Dentists | ||
| 653 | |a Dental prosthetics | ||
| 653 | |a Decision making | ||
| 653 | |a Self-assessment | ||
| 700 | 1 | |a Philipp del Hougne | |
| 700 | 1 | |a Isabella Di Lorenzo | |
| 700 | 1 | |a Höhne, Christian | |
| 700 | 1 | |a Schrenker, Johannes | |
| 700 | 1 | |a Schmitter, Marc | |
| 773 | 0 | |t BMC Oral Health |g vol. 25 (2025), p. 1-11 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3247129133/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3247129133/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3247129133/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |