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![The aortic root. a) healthy state b) dilated state c) reconstructed state [3].](publication/281788079/figure/fig1/AS:11431281122987357@1677599282810/The-aortic-root-a-healthy-state-b-dilated-state-c-reconstructed-state-3.jpg)
Source publication
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 unkno...
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Citations
... By estimating this mapping, it is possible to predict the healthy features of a valve based on its dilated features. Thus, surgery planning can be described as a learning problem, which can be solved using machine learning algorithms [4]. For this purpose, we built up an experimental data base with images of aortic roots in healthy and dilated states (cf II-A). ...
Valve-sparing aortic root reconstruction is an up- and-coming approach for patients suffering from aortic valve insufficiencies which promises to significantly reduce complications. However, the success of the treatment strongly depends on the challenging task of choosing the correct size of the prosthesis, for which, up to now, surgeons solely have to rely on their experience. Here, we present a novel machine learning based approach, which might make it possible to predict the size of the prosthesis from pre-operatively acquired ultrasound images. We utilize support vector regression to train a prediction model on three geometric features extracted from the ultrasound data. In order to evaluate the accuracy and robustness of our approach we created a large data base of porcine aortic root geometries in a healthy state and an artificially dilated state. Our results indicate that prediction of correct prosthesis sizes is feasible. Furthermore, they suggest that it is crucial that the training data set faithfully represents the diversity of aortic root geometries.
