Evidence map›Paper›PMID 33825985›Full record

ArticleInsights into imaging2021

Three-dimensional preoperative planning in the weight-bearing state: validation and clinical evaluation.

Tabitha Roth, Fabio Carrillo, Matthias Wieczorek, Giulia Ceschi, Hooman Esfandiari, Reto Sutter, Lazaros Vlachopoulos, Wolfgang Wein, Sandro F Fucentese, Philipp Fürnstahl

Abstract read
In one paragraph

Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  16. The winking sign is an indicator for increased femorotibial rotation in patients with recurrent patellar instability.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tabitha RothInstitute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland. tabitha.roth@balgrist.ch.ORCID http://orcid.org/0000-0002-2342-9710
Fabio CarrilloResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008, Zurich, Switzerland.
Matthias WieczorekImFusion GmbH, Agnes-Pockels-Bogen 1, 80992, Munich, Germany.
Giulia CeschiResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008, Zurich, Switzerland.
Hooman EsfandiariResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008, Zurich, Switzerland.
Reto SutterDepartment of Radiology, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland.
Lazaros VlachopoulosDepartment of Orthopedics, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland.
Wolfgang WeinImFusion GmbH, Agnes-Pockels-Bogen 1, 80992, Munich, Germany.
Sandro F FucenteseDepartment of Orthopedics, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland.
Philipp FürnstahlResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008, Zurich, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 320030_182352 / 1
6 · The paper itself

Abstract

objectives3D preoperative planning of lower limb osteotomies has become increasingly important in light of modern surgical technologies. However, 3D models are usually reconstructed from Computed Tomography data acquired in a non-weight-bearing posture and thus neglecting the positional variations introduced by weight-bearing. We developed a registration and planning pipeline that allows for 3D preoperative planning and subsequent 3D assessment of anatomical deformities in weight-bearing conditions.

methodsAn intensity-based algorithm was used to register CT scans with long-leg standing radiographs and subsequently transform patient-specific 3D models into a weight-bearing state. 3D measurement methods for the mechanical axis as well as the joint line convergence angle were developed. The pipeline was validated using a leg phantom. Furthermore, we evaluated our methods clinically by applying it to the radiological data from 59 patients.

resultsThe registration accuracy was evaluated in 3D and showed a maximum translational and rotational error of 1.1 mm (mediolateral direction) and 1.2° (superior-inferior axis). Clinical evaluation proved feasibility on real patient data and resulted in significant differences for 3D measurements when the effects of weight-bearing were considered. Mean differences were 2.1 ± 1.7° and 2.0 ± 1.6° for the mechanical axis and the joint line convergence angle, respectively. 37.3 and 40.7% of the patients had differences of 2° or more in the mechanical axis or joint line convergence angle between weight-bearing and non-weight-bearing states.

conclusionsOur presented approach provides a clinically feasible approach to preoperatively fuse 2D weight-bearing and 3D non-weight-bearing data in order to optimize the surgical correction.

Indexed as

2D–3D registration3D measurement methods3D preoperative planningOsteotomyWeight-bearing

Identifiers

PMID33825985
PMCPMC8026795

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.