Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Taghizadeh, Elham; Kistler, Michael; Büchler, Philippe; Reyes, Mauricio (2016). Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling. In: Joldes, Grand; Doyle, Barry; Wittek, Adam; Nielsen, Poul; Miller, Karol (eds.) Computational Biomechanics for Medicine X (pp. 115-127). Springer-Verlag New York

[img] Text
Taghizadeh et al. - MICCAI 2015 Workshop Proceedings - Computational Biomechanics for Medicine X - 2015.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (1MB) | Request a copy

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Taghizadeh, Elham; Kistler, Michael; Büchler, Philippe and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-3-319-28327-2

Publisher:

Springer-Verlag New York

Submitter:

Philippe Büchler

Date Deposited:

20 Apr 2016 09:49

Last Modified:

09 Sep 2017 05:29

BORIS DOI:

10.7892/boris.75594

URI:

https://boris.unibe.ch/id/eprint/75594

Actions (login required)

Edit item Edit item
Provide Feedback