Imagen de Portada

Toward artificial intelligence in dental prosthesis planning — a preliminary in-silico feasibility study

Guardado en:
Publicado en:BMC Oral Health vol. 25 (2025), p. 1-11
Autor principal: Michael del Hougne
Otros Autores: Philipp del Hougne, Isabella Di Lorenzo, Höhne, Christian, Schrenker, Johannes, Schmitter, Marc
Publicado:
Springer Nature B.V.
Materias:
Python Software Foundation
Prosthodontics
Software
Artificial intelligence
Datasets
Prostheses
Planning
Neural networks
Neurosciences
Dentures
Automation
Feasibility studies
Python
Teeth
Dentists
Dental prosthetics
Decision making
Self-assessment
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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.
ISSN:1472-6831
DOI:10.1186/s12903-025-06778-6
Fuente:Health & Medical Collection