Using Neural Networks to Predict the Trabecular Arrangement in the Proximal Femur

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Pais, I (Autor) Outra; Jorge Lino (Autor) FEUP; Belinha, J (Autor) Outra

Ata de Conferência Internacional

Título: Lecture Notes in Bioengineering

Páginas: 41-48

10th Congress of the Portuguese Society of Biomechanics, CNB 2023

Figueira da Foz, 5 May 2023 through 6 May 2023

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-6E6

DOI: 10.1007/978-3-031-47790-4_4

Abstract (EN): Differences in the anatomy of this bone occur naturally in the population and will lead to different structural responses which then lead to different trabecular bone arrangements. These can be predicted through computational analysis which the literature has shown to deliver accurate results in agreement to medical imaging. Machine learning techniques can be combined with the FEM to reduce the computational cost associated with the prediction of the remodelling process. In this work it is shown that neural networks achieved accurate solutions in a fraction of the time being the main disadvantage the need to gather high volumes of data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7