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Concentric Tube Robot Optimization and Path Planning for Epilepsy Surgeries

2023· article· en· W4378174520 on OpenAlexaff
Zhiling Zou, Jessica Burgner-Kahrs, Thomas Looi, James M. Drake

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRobotConcentricComputer scienceNeurosurgerySurgical robotSurgical planningMotion planningSurgical proceduresPath (computing)Artificial intelligenceSurgeryMedicineMathematics

Abstract

fetched live from OpenAlex

Although robot-assisted minimally invasive surgery (MIS) has been widely investigated in recent years, limited access to the surgical sites in children's small anatomic cavities with current surgical tools is still a major concern in pediatric neurosurgery. Concentric tube robots (CTR), a class of continuum robot with flexible backbones and small diameters, are potential solutions to this problem. To demonstrate CTRs' use in epilepsy surgeries, this work proposes an evolutionary-based optimization framework, which could be used to find optimal patient-specific and procedure-specific CTR design parameters and surgical paths subject to anatomical constraints. Three sets of real patient data are used to evaluate this framework, and the validity of the generated surgical plans are verified by neurosurgeons, the platform's primary users.

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: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.192

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 head0.243
Teacher spread0.222 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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