Abstract
Finding the individually optimal prosthesis size is an intricate task during valve-sparing aortic root reconstruction. Previous work has shown that machine learning based prosthesis size prediction is possible. However, the very high demands on the underlying training data set prevent the application in a clinical setting. In this work, the authors present an alternative approach combining simplified deformation modeling with machine learning to mimic the surgeon’s decision making process. Compared to the previously published approach, the new method provides a similar prediction accuracy whith a dramatic decrease of demand on the training data. This is an important step towards the clinical application of machine learning based planning of personalized valve-sparing aortic root reconstruction.
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Acknowledgement
The authors would like to thank Ingvild Detjens and Erik Werrmann for their help conducting the experiments. This publication is a result of the ongoing research within the LUMEN research group, which is funded by the German Bundesministerium für Bildung und Forschung (BMBF) (FKZ 13EZ1140A/B). LUMEN is a joint research project of Lübeck University of Applied Sciences and University of Lübeck and represents an own branch of the Graduate School for Computing in Medicine and Life Sciences of University of Luübeck.
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Hagenah, J., Scharfschwerdt, M., Schweikard, A., Metzner, C. (2017). Combining Deformation Modeling and Machine Learning for Personalized Prosthesis Size Prediction in Valve-Sparing Aortic Root Reconstruction. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_44
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DOI: https://doi.org/10.1007/978-3-319-59448-4_44
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