University of Hertfordshire

  • Jannis Hagenah
  • Michael Scharfschwerdt
  • Achim Schweikard
  • Christoph Metzner
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Original languageEnglish
Title of host publicationFunctional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
PublisherSpringer Verlag
Number of pages10
Volume10263 LNCS
ISBN (Print)9783319594477
Publication statusPublished - 2017
Event9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada
Duration: 11 Jun 201713 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10263 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017


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.

ID: 12043987