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dc.contributor.author
Roth, Tabitha
dc.contributor.author
Sigrist, Bastian
dc.contributor.author
Wieczorek, Matthias
dc.contributor.author
Schilling, Nathanael
dc.contributor.author
Hodel, Sandro
dc.contributor.author
Walker, Jonas
dc.contributor.author
Somm, Mario
dc.contributor.author
Wein, Wolfgang
dc.contributor.author
Sutter, Reto
dc.contributor.author
Vlachopoulos, Lazaros
dc.contributor.author
Snedeker, Jess G.
dc.contributor.author
Fucentese, Sandro F.
dc.contributor.author
Fürnstahl, Philipp
dc.contributor.author
Carrillo, Fabio
dc.date.accessioned
2023-06-07T09:14:15Z
dc.date.available
2023-06-04T04:32:33Z
dc.date.available
2023-06-07T09:11:54Z
dc.date.available
2023-06-07T09:14:15Z
dc.date.issued
2023-12-31
dc.identifier.issn
2469-9322
dc.identifier.other
10.1080/24699322.2023.2211728
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/615048
dc.identifier.doi
10.3929/ethz-b-000615048
dc.description.abstract
3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Taylor & Francis
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
3D surgical planning
en_US
dc.subject
automatic
en_US
dc.subject
high tibial osteotomy
en_US
dc.subject
multi-objective optimization
en_US
dc.title
An automated optimization pipeline for clinical-grade computer-assisted planning of high tibial osteotomies under consideration of weight-bearing
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2023-05-16
ethz.journal.title
Computer Assisted Surgery
ethz.journal.volume
28
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
2211728
en_US
ethz.size
20 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02518 - Institut für Biomechanik / Institute for Biomechanics::03822 - Snedeker, Jess G. / Snedeker, Jess G.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02518 - Institut für Biomechanik / Institute for Biomechanics::03822 - Snedeker, Jess G. / Snedeker, Jess G.
ethz.date.deposited
2023-06-04T04:32:38Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-06-07T09:11:55Z
ethz.rosetta.lastUpdated
2024-02-02T23:56:01Z
ethz.rosetta.versionExported
true
ethz.COinS
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