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Design of a Minimally Invasive Lung Tumor Localization Device

2012· article· en· W2011917784 on OpenAlexafffund
T.P. Kurowski, Ana Luisa Trejos, Michael D. Naish, Rajni V. Patel, Richard Malthaner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaLondon Health Sciences Centre
KeywordsPalpationRobot end effectorComputer scienceProcess (computing)Finite element methodBiomedical engineeringArtificial intelligenceComputer visionEngineeringRobotMedicineSurgery

Abstract

fetched live from OpenAlex

This paper describes the design, analysis, and experimental validation of a novel minimally invasive instrument for lung tumor localization. The instrument end effector is a two-degree of freedom lung tissue palpator. It allows for optimal tissue palpation to increase useful sensor feedback by ensuring sensor contact, and prevents tissue damage by uniformly distributing pressure on the tissue. Finite element analysis was used to guide the design process, resulting in a final design that could achieve a factor of safety of 4 for a 20 N force acting on the end effector—the approximate weight of a human lung. Validation experiments were conducted on a prototype instrument to assess its articulation and load-carrying capacity. The end effector design allows for the inclusion of ultrasound, tactile, and kinesthetic sensors. It is expected that this device will form the basis for robotics-assisted palpation and increase the likelihood of positive tumor localization.Copyright © 2012 by ASME

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.234
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2012
Admission routes2
Has abstractyes

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