Evidence map›Paper›PMID 27425646›Full record

ArticleMedical image analysis2016

From computer-assisted intervention research to clinical impact: The need for a holistic approach.

Sébastien Ourselin, Mark Emberton, Tom Vercauteren

Abstract readEditorial
In one paragraph

Article in Medical image analysis, 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
–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

2 citing papers in PubMed.

  1. Medical device regulatory challenges in the UK are affecting innovation and its potential benefits.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2023
    Article
  2. SnappySonic: An Ultrasound Acquisition Replay Simulator.Journal of open research software · 2020
    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

3 authors.

Sébastien OurselinTranslational Imaging Group, Centre for Medical Image Computing, Dept. of Medical Physics & Biomedical Engineering, University College London, London, UK. Electronic address: s.ourselin@ucl.ac.uk.
Mark EmbertonDivision of Surgery & Interventional Science, University College London, London, UK; University College Hospitals NHS Foundation Trust, London, UK. Electronic address: m.emberton@ucl.ac.uk.
Tom VercauterenTranslational Imaging Group, Centre for Medical Image Computing, Dept. of Medical Physics & Biomedical Engineering, University College London, London, UK. Electronic address: t.vercauteren@ucl.ac.uk.

Funding

Medical Research Council MR/M009092/1Wellcome Trust 101957Wellcome Trust WT101957Wellcome Trust WT106882
6 · The paper itself

Abstract

The early days of the field of medical image computing (MIC) and computer-assisted intervention (CAI), when publishing a strong self-contained methodological algorithm was enough to produce impact, are over. As a community, we now have substantial responsibility to translate our scientific progresses into improved patient care. In the field of computer-assisted interventions, the emphasis is also shifting from the mere use of well-known established imaging modalities and position trackers to the design and combination of innovative sensing, elaborate computational models and fine-grained clinical workflow analysis to create devices with unprecedented capabilities. The barriers to translating such devices in the complex and understandably heavily regulated surgical and interventional environment can seem daunting. Whether we leave the translation task mostly to our industrial partners or welcome, as researchers, an important share of it is up to us. We argue that embracing the complexity of surgical and interventional sciences is mandatory to the evolution of the field. Being able to do so requires large-scale infrastructure and a critical mass of expertise that very few research centres have. In this paper, we emphasise the need for a holistic approach to computer-assisted interventions where clinical, scientific, engineering and regulatory expertise are combined as a means of moving towards clinical impact. To ensure that the breadth of infrastructure and expertise required for translational computer-assisted intervention research does not lead to a situation where the field advances only thanks to a handful of exceptionally large research centres, we also advocate that solutions need to be designed to lower the barriers to entry. Inspired by fields such as particle physics and astronomy, we claim that centralised very large innovation centres with state of the art technology and health technology assessment capabilities backed by core support staff and open interoperability standards need to be accessible to the wider computer-assisted intervention research community.

Indexed as

Biomedical ResearchHealth Impact AssessmentTherapy, Computer-AssistedAlgorithmsHumansWorkflowComputer-assisted interventionHealth technology assessmentMedical devicesValley of death

Identifiers

PMID27425646
PMCPMC6736672

What OpenQuestion holds

Textmetadata
LicenceTDM
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.