| [85610] |
| Title: A machine learning approach for planning valve-sparing aortic root reconstruction. |
| Written by: J. Hagenah and M. Scharfschwerdt and A. Schlaefer and C. Metzner |
| in: <em>Current Directions in Biomedical Engineering</em>. September (2015). |
| Volume: <strong>1</strong>. Number: (1), |
| on pages: 361-365 |
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| DOI: 10.1515/cdbme-2015-0089 |
| URL: http://www.degruyter.com/dg/viewarticle.fullcontentlink:pdfeventlink/$002fj$002fcdbme.2015.1.issue-1$002fcdbme-2015-0089$002fcdbme-2015-0089.pdf?result=3&rskey=AEqZpl&t:ac=j$002fcdbme.2015.1.issue-1$002fcdbme-2015-0089$002fcdbme-2015-0089.xml |
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Note: ISSN 2364-5504
Abstract: Choosing the optimal prosthesis size and shape is a difficult task during surgical valve-sparing aortic root reconstruction. Hence, there is a need for surgery planning tools. Common surgery planning approaches try to model the mechanical behaviour of the aortic valve and its leaflets. However, these approaches suffer from inaccuracies due to unknown biomechanical properties and from a high computational complexity. In this paper, we present a new approach based on machine learning that avoids these problems. The valve geometry is described by geometrical features obtained from ultrasound images. We interpret the surgery planning as a learning problem, in which the features of the healthy valve are predicted from these of the dilated valve using support vector regression (SVR). Our first results indicate that a machine learning based surgery planning can be possible.