Evidence map›Paper›PMID 34012980›Full record

ArticleFrontiers in robotics and AI2021

Design and Modelling of a Continuum Robot for Distal Lung Sampling in Mechanically Ventilated Patients in Critical Care.

Zisos Mitros, Balint Thamo, Christos Bergeles, Lyndon da Cruz, Kevin Dhaliwal, Mohsen Khadem

Open access · goldAbstract read
In one paragraph

Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 27% 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

5 citing papers in PubMed, 22 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

6 authors at 4 institutions in 1 country.

Zisos MitrosRobotics and Vision in Medicine (RViM) Lab, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Balint ThamoSchool of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
Christos BergelesRobotics and Vision in Medicine (RViM) Lab, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Lyndon da CruzWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
Kevin DhaliwalTranslational Healthcare Technologies Group in the Centre for Inflammation Research, Queen's Medical Research Institute, Edinburgh, United Kingdom.
Mohsen KhademSchool of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
King's College London · GBUniversity of Edinburgh · GBQueen's Medical Centre · GBUniversity College London · GB

Funding

Medical Research Council MC_PC_18046Medical Research Council MR/T023252/1
6 · The paper itself

Abstract

In this paper, we design and develop a novel robotic bronchoscope for sampling of the distal lung in mechanically-ventilated (MV) patients in critical care units. Despite the high cost and attributable morbidity and mortality of MV patients with pneumonia which approaches 40%, sampling of the distal lung in MV patients suffering from range of lung diseases such as Covid-19 is not standardised, lacks reproducibility and requires expert operators. We propose a robotic bronchoscope that enables repeatable sampling and guidance to distal lung pathologies by overcoming significant challenges that are encountered whilst performing bronchoscopy in MV patients, namely, limited dexterity, large size of the bronchoscope obstructing ventilation, and poor anatomical registration. We have developed a robotic bronchoscope with 7 Degrees of Freedom (DoFs), an outer diameter of 4.5 mm and inner working channel of 2 mm. The prototype is a push/pull actuated continuum robot capable of dexterous manipulation inside the lung and visualisation/sampling of the distal airways. A prototype of the robot is engineered and a mechanics-based model of the robotic bronchoscope is developed. Furthermore, we develop a novel numerical solver that improves the computational efficiency of the model and facilitates the deployment of the robot. Experiments are performed to verify the design and evaluate accuracy and computational cost of the model. Results demonstrate that the model can predict the shape of the robot in <0.011s with a mean error of 1.76 cm, enabling the future deployment of a robotic bronchoscope in MV patients.

Indexed as

flexible robotmathematical modellingrobotic bronchoscopesteerable cathetersurgical robot

Identifiers

PMID34012980
PMCPMC8126695
OpenAlexW3159956094

What OpenQuestion holds

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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.