Evidence map›Paper›PMID 30293172›Full record

ArticleInternational journal of computer assisted radiology and surgery2019

Tactile sensor-based real-time clustering for tissue differentiation.

Ralf Stroop, Makoto Nakamura, Johan Schoukens, David Oliva Uribe

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Article in International journal of computer assisted radiology and surgery, 2019. 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
0.1field-weighted citation impact, top 54% 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

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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

4 authors at 2 institutions in 2 countries.

Ralf StroopDepartment of Neurosurgery, Academic Hospital Cologne-Merheim, Cologne, Germany.
Makoto NakamuraDepartment of Neurosurgery, Academic Hospital Cologne-Merheim, Cologne, Germany.
Johan SchoukensDepartment of Engineering Technology (INDI), Vrije Universiteit Brussel, Brussels, Belgium.
David Oliva UribeDepartment of Engineering Technology (INDI), Vrije Universiteit Brussel, Brussels, Belgium. david.oliva.uribe@vub.be.ORCID http://orcid.org/0000-0002-5532-7145
University Hospital Cologne · DEVrije Universiteit Brussel · BE

Funding

European Research Council 320378 - SNLSID
6 · The paper itself

Abstract

purposeReliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon's visual aspect and tactile impression of the considered tissue, which is-due to inherent low brain consistency contrast-a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when-as for neuroendoscopy-tactile perception will be damped or-as for surgical robotic applications-will not be a priori existent. Here, we present the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.

methodsA robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E

resultsCluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.

conclusionsBimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising.

Indexed as

AlgorithmsTouchAnimalsBrainBrain NeoplasmsHumansRoboticsSwineViscosityBimorphBrain tumor resectionMultisine excitationTactile sensorTissue differentiation

Identifiers

PMID30293172
OpenAlexW2895331841

What OpenQuestion holds

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Read underepoch 390

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.