Evidence map›Paper›PMID 34024940›Full record

ArticleJournal of physics D: Applied physics2021

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation.

Michael Ebner, Eli Nabavi, Jonathan Shapey, Yijing Xie, Florentin Liebmann, José Miguel Spirig, Armando Hoch, Mazda Farshad, Shakeel R Saeed, Robert Bradford and 5 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of physics D: Applied physics, 2021. 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
3.6field-weighted citation impact, top 7% 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, 32 citations in OpenAlex.

  1. Pooled it
  2. Neural illumination calibration for surgical workflow-optimized spectral imaging.International journal of computer assisted radiology and surgery · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
    Article
  7. Synthetic white balancing for intra-operative hyperspectral imaging.Journal of medical imaging (Bellingham, Wash.) · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstruction.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2022
    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

15 authors at 5 institutions in 2 countries.

Michael EbnerSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-0170-7062
Eli NabaviSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Jonathan ShapeySchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-0291-348X
Yijing XieSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-3432-8587
Florentin LiebmannResearch in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland.
José Miguel SpirigDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Armando HochDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Mazda FarshadDepartment of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-7190-1127
Shakeel R SaeedDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom.
Robert BradfordDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom.
Iain YardleyDepartment of Paediatric Surgery, Evelina London Children's Hospital, London, United Kingdom.
Sébastien OurselinSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-5694-5340
A David EdwardsSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-4801-7066
Philipp FührnstahlResearch in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland.
Tom VercauterenSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-1794-0456
King's College London · GBUniversity of Zurich · CHEvelina London Children's Healthcare · GBNational Hospital for Neurology and Neurosurgery · GBRoyal National Throat Nose and Ear Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and

Indexed as

computer assisted interventionsexoscopefirst-in-patienthyperspectral imagingmedical devicetranslational research

Identifiers

PMID34024940
PMCPMC8132621
OpenAlexW3158633215

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.