Evidence map›Paper›PMID 29551011›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

IMHOTEP: virtual reality framework for surgical applications.

Micha Pfeiffer, Hannes Kenngott, Anas Preukschas, Matthias Huber, Lisa Bettscheider, Beat Müller-Stich, Stefanie Speidel

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
4.5field-weighted citation impact, top 5% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Liver mask-guided SAM-enhanced dual-decoder network for landmark segmentation in AR-guided surgery.International journal of computer assisted radiology and surgery · 2026
    Article
  4. Two-stream MeshCNN for key anatomical segmentation on the liver surface.International journal of computer assisted radiology and surgery · 2025
    Article
  5. Gait patterns during overground and virtual omnidirectional treadmill walking.Journal of neuroengineering and rehabilitation · 2024
    Article
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  14. AR in VR: assessing surgical augmented reality visualizations in a steerable virtual reality environment.International journal of computer assisted radiology and surgery · 2018
    Article
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

7 authors at 3 institutions in 1 country.

Micha PfeifferNational Center for Tumor Diseases, Dresden, Germany. micha.pfeiffer@nct-dresden.de.ORCID http://orcid.org/0000-0001-9822-8641
Hannes KenngottDepartment of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Anas PreukschasDepartment of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Matthias HuberInstitute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Lisa BettscheiderDepartment of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Beat Müller-StichDepartment of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
Stefanie SpeidelNational Center for Tumor Diseases, Dresden, Germany.
Heidelberg University · DENational Center for Tumor Diseases · DEKarlsruhe Institute of Technology · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe data which is available to surgeons before, during and after surgery is steadily increasing in quantity as well as diversity. When planning a patient's treatment, this large amount of information can be difficult to interpret. To aid in processing the information, new methods need to be found to present multimodal patient data, ideally combining textual, imagery, temporal and 3D data in a holistic and context-aware system.

methodsWe present an open-source framework which allows handling of patient data in a virtual reality (VR) environment. By using VR technology, the workspace available to the surgeon is maximized and 3D patient data is rendered in stereo, which increases depth perception. The framework organizes the data into workspaces and contains tools which allow users to control, manipulate and enhance the data. Due to the framework's modular design, it can easily be adapted and extended for various clinical applications.

resultsThe framework was evaluated by clinical personnel (77 participants). The majority of the group stated that a complex surgical situation is easier to comprehend by using the framework, and that it is very well suited for education. Furthermore, the application to various clinical scenarios-including the simulation of excitation propagation in the human atrium-demonstrated the framework's adaptability. As a feasibility study, the framework was used during the planning phase of the surgical removal of a large central carcinoma from a patient's liver.

conclusionThe clinical evaluation showed a large potential and high acceptance for the VR environment in a medical context. The various applications confirmed that the framework is easily extended and can be used in real-time simulation as well as for the manipulation of complex anatomical structures.

Indexed as

Virtual RealityAgedCarcinoma, HepatocellularComputer SimulationFeasibility StudiesFemaleGeneral SurgeryHepatectomyHumansImaging, Three-DimensionalInternship and ResidencyLiver NeoplasmsPilot ProjectsSimulation TrainingStudents, MedicalSurgical Procedures, OperativeAdvanced medical visualizationSurgical planningVirtual reality

Identifiers

PMID29551011
OpenAlexW2790951728

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.