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Record W2889232616 · doi:10.1115/1.4041335

Real-Time Mechanical-Encoding of Needle Shape for Image-Guided Medical and Surgical Interventions

2018· article· en· W2889232616 on OpenAlexaff
Mohamad Abbass, S. Fan, Kevin Barker, Aaron Fenster, Jeremy Cepek

Bibliographic record

VenueJournal of Medical Devices · 2018
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDeflection (physics)AcousticsComputer scienceComputer visionBiomedical engineeringSimulationMaterials scienceOpticsMedicinePhysics

Abstract

fetched live from OpenAlex

Error and uncertainty in needle placement can drastically impact the clinical outcome of both diagnostic and therapeutic needle-based procedures. In this work, we aim to estimate the shape of a bent needle during insertion and provide a prototype design of a needle whose deflection is tracked in real time. We calculate slope along a needle by measuring the movement of fixed wires running along its length with a compact image-based sensor. Through the use of the Euler–Bernoulli beam theory, we calculate shape and trajectory of a needle. We constructed a prototype needle with two wires fixed along its length and measured wire-movement using a vertical-cavity surface-emitting laser (VCSEL) mouse sensor. This method was able to estimate needle tip deflection within 1 mm in a variety of deflection scenarios in real time. We then provide a design of a needle with real-time deflection tracking in 3D, providing the user with a simple display to convey needle deflection in tissue. This method could be applied to needle-based biopsy or therapy procedures to improve the diagnostic accuracy or treatment delivery quality.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.036
GPT teacher head0.341
Teacher spread0.306 · 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.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2018
Admission routes1
Has abstractyes

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