A machine learning approach for planning valve-sparing aortic root reconstruction

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.

Subjects

aortic root reconstruction

transesophageal ultrasound

surgery planning

support vector regression

machine learning

DDC Class

617.9: Operative Surgery and Special Fields of Surgery

006.3: Artificial Intelligence

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publishedVersion

Lizenz

https://creativecommons.org/licenses/by-nd/3.0/

Name

10.1515_cdbme-2015-0089.pdf

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464.2 KB

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Adobe PDF

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A machine learning approach for planning valve-sparing aortic root reconstruction