University of Hertfordshire

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Combining deformation modeling and machine learning for personalized prosthesis size prediction in valve-sparing aortic root reconstruction. / Hagenah, Jannis; Scharfschwerdt, Michael; Schweikard, Achim; Metzner, Christoph.

Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS Springer Verlag, 2017. p. 461-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

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 Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. vol. 10263 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10263 LNCS, Springer Verlag, pp. 461-470, 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017, Toronto, Canada, 11/06/17. https://doi.org/10.1007/978-3-319-59448-4_44

APA

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 Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings (Vol. 10263 LNCS, pp. 461-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59448-4_44

Vancouver

Hagenah J, Scharfschwerdt M, Schweikard A, Metzner C. Combining deformation modeling and machine learning for personalized prosthesis size prediction in valve-sparing aortic root reconstruction. In Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS. Springer Verlag. 2017. p. 461-470. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59448-4_44

Author

Hagenah, Jannis ; Scharfschwerdt, Michael ; Schweikard, Achim ; Metzner, Christoph. / Combining deformation modeling and machine learning for personalized prosthesis size prediction in valve-sparing aortic root reconstruction. Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Vol. 10263 LNCS Springer Verlag, 2017. pp. 461-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{574a627e24b2428d8a44b8832ac9f739,
title = "Combining deformation modeling and machine learning for personalized prosthesis size prediction in valve-sparing aortic root reconstruction",
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{\textquoteright}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.",
keywords = "Aortic valve, Computer assisted surgery, Machine learning, Personalized medicine, Valve-sparing aortic root reconstruction",
author = "Jannis Hagenah and Michael Scharfschwerdt and Achim Schweikard and Christoph Metzner",
year = "2017",
doi = "10.1007/978-3-319-59448-4_44",
language = "English",
isbn = "9783319594477",
volume = "10263 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "461--470",
booktitle = "Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings",
address = "Germany",
note = "9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 ; Conference date: 11-06-2017 Through 13-06-2017",

}

RIS

TY - GEN

T1 - Combining deformation modeling and machine learning for personalized prosthesis size prediction in valve-sparing aortic root reconstruction

AU - Hagenah, Jannis

AU - Scharfschwerdt, Michael

AU - Schweikard, Achim

AU - Metzner, Christoph

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Aortic valve

KW - Computer assisted surgery

KW - Machine learning

KW - Personalized medicine

KW - Valve-sparing aortic root reconstruction

UR - http://www.scopus.com/inward/record.url?scp=85020432373&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59448-4_44

DO - 10.1007/978-3-319-59448-4_44

M3 - Conference contribution

AN - SCOPUS:85020432373

SN - 9783319594477

VL - 10263 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 461

EP - 470

BT - Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings

PB - Springer Verlag

T2 - 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017

Y2 - 11 June 2017 through 13 June 2017

ER -