Evidence map›Paper›PMID 27072836›Full record

ArticleInternational journal of computer assisted radiology and surgery2016

Comprehensive patient-specific information preprocessing for cardiac surgery simulations.

N Schoch, F Kißler, M Stoll, S Engelhardt, R de Simone, I Wolf, R Bendl, V Heuveline

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Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
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

2 citing papers in PubMed, 8 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

N SchochEngineering Mathematics and Computing Lab (EMCL), Heidelberg University, Heidelberg, Germany. nicolai.schoch@iwr.uni-heidelberg.de.
F KißlerEngineering Mathematics and Computing Lab (EMCL), Heidelberg University, Heidelberg, Germany.
M StollMedical Physics in Radiation Oncology, DKFZ Heidelberg, Heidelberg, Germany.
S EngelhardtMedical and Biological Informatics, DKFZ Heidelberg, Heidelberg, Germany.
R de SimoneCardiac Surgery, University Hospital Heidelberg, Heidelberg, Germany.
I WolfMedical and Biological Informatics, DKFZ Heidelberg, Heidelberg, Germany.
R BendlMedical Physics in Radiation Oncology, DKFZ Heidelberg, Heidelberg, Germany.
V HeuvelineEngineering Mathematics and Computing Lab (EMCL), Heidelberg University, Heidelberg, Germany.
Heidelberg University · DEDKFZ-ZMBH Alliance · DEHeidelberger Institut für Radioonkologie · DEUniversity Hospital Heidelberg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePatient-specific biomechanical simulations of the behavior of soft tissue gain importance in current surgery assistance systems as they can provide surgeons with valuable ancillary information for diagnosis and therapy. In this work, we aim at supporting minimally invasive mitral valve reconstruction (MVR) surgery by providing scenario setups for FEM-based soft tissue simulations, which simulate the behavior of the patient-individual mitral valve subject to natural forces during the cardiac cycle after an MVR. However, due to the complexity of these simulations and of their underlying mathematical models, it is difficult for non-engineers to sufficiently understand and adequately interpret all relevant modeling and simulation aspects. In particular, it is challenging to set up such simulations in automated preprocessing workflows such that they are both patient-specific and still maximally comprehensive with respect to the model.

methodsIn this paper, we address this issue and present a fully automated chain of preprocessing operators for setting up comprehensive, patient-specific biomechanical models on the basis of patient-individual medical data. These models are suitable for FEM-based MVR surgery simulation. The preprocessing methods are integrated into the framework of the Medical Simulation Markup Language and allow for automated information processing in a data-driven pipeline.

resultsWe constructed a workflow for holistic, patient-individual information preprocessing for MVR surgery simulations. In particular, we show how simulation preprocessing can be both fully automated and still patient-specific, when using a series of dedicated MVR data analytics operators. The outcome of our operator chain is visualized in order to help the surgeon understand the model setup.

conclusionWith this work, we expect to improve the usability of simulation-based MVR surgery assistance, through allowing for fully automated, patient-specific simulation setups. Combined visualization of the biomechanical model setup and of the corresponding surgery simulation results fosters the understandability and transparency of our assistance environment.

Indexed as

Models, AnatomicPatient-Specific ModelingBiomechanical PhenomenaCardiac Surgical ProceduresHumansMinimally Invasive Surgical ProceduresMitral ValveMitral Valve AnnuloplastyMitral Valve InsufficiencyAutomated information processingCardiac surgerySimulation-based surgery assistanceSimulation preprocessingTreatment planning

Identifiers

PMID27072836
OpenAlexW2339803386

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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.