Machine learning enhances assessment of proficiency in endovascular aortic repair simulations

Rebecca Andrea Conradsen Skov; Jonathan Lawaetz; Michael Strøm; Isabelle Van Herzeele; Lars Konge; Timothy Andrew Resch; Jonas Peter Eiberg; ENHANCE research collaborators

doi:10.1016/j.cpsurg.2024.101576

Machine learning enhances assessment of proficiency in endovascular aortic repair simulations

Rebecca Andrea Conradsen Skov^*, Jonathan Lawaetz, Michael Strøm, Isabelle Van Herzeele, Lars Konge, Timothy Andrew Resch, Jonas Peter Eiberg, ENHANCE research collaborators

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Original language	English
Article number	101576
Journal	Current Problems in Surgery
Volume	61
Issue number	10
Number of pages	13
ISSN	0011-3840
DOIs	https://doi.org/10.1016/j.cpsurg.2024.101576
Publication status	Published - 2024

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Machine learning enhances assessment of proficiency in endovascular aortic repair simulations. / Skov, Rebecca Andrea Conradsen; Lawaetz, Jonathan; Strøm, Michael; Van Herzeele, Isabelle; Konge, Lars ; Resch, Timothy Andrew ; Eiberg, Jonas Peter; ENHANCE research collaborators.

In: Current Problems in Surgery, Vol. 61, No. 10, 101576, 2024.

Research output: Contribution to journal › Journal article › Research › peer-review

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title = "Machine learning enhances assessment of proficiency in endovascular aortic repair simulations",

author = "Skov, {Rebecca Andrea Conradsen} and Jonathan Lawaetz and Michael Str{\o}m and {Van Herzeele}, Isabelle and Lars Konge and Resch, {Timothy Andrew} and Eiberg, {Jonas Peter} and {ENHANCE research collaborators}",

note = "Funding Information: IvH is supported by a Senior Clinical Fellowship (802314N) of the Fund for Scientific Research - Flanders, Belgium. This study was supported by an unrestricted general research grant from Medtronic (A1731784). The funder did not have any role in design of the study, data collection, analysis, interpretation, manuscript preparation or decision to publish the study.",

year = "2024",

doi = "10.1016/j.cpsurg.2024.101576",

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T1 - Machine learning enhances assessment of proficiency in endovascular aortic repair simulations

AU - Skov, Rebecca Andrea Conradsen

AU - Lawaetz, Jonathan

AU - Strøm, Michael

AU - Van Herzeele, Isabelle

AU - Konge, Lars

AU - Resch, Timothy Andrew

AU - Eiberg, Jonas Peter

AU - ENHANCE research collaborators

N1 - Funding Information: IvH is supported by a Senior Clinical Fellowship (802314N) of the Fund for Scientific Research - Flanders, Belgium. This study was supported by an unrestricted general research grant from Medtronic (A1731784). The funder did not have any role in design of the study, data collection, analysis, interpretation, manuscript preparation or decision to publish the study.

PY - 2024

Y1 - 2024

U2 - 10.1016/j.cpsurg.2024.101576

DO - 10.1016/j.cpsurg.2024.101576

M3 - Journal article

AN - SCOPUS:85202476258

VL - 61

JO - Current Problems in Surgery

JF - Current Problems in Surgery

SN - 0011-3840

IS - 10

M1 - 101576

ER -