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Record W2331492476 · doi:10.1142/s2424905x1640002x

Ultrasound-Based Image Guidance and Motion Compensating Control for Robot-Assisted Beating-Heart Surgery

2016· article· en· W2331492476 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Medical Robotics Research · 2016
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationUniversity of Alberta
KeywordsTeleoperationTask (project management)Computer visionUltrasoundOrientation (vector space)Computer scienceArtificial intelligenceMedicineRobotRadiologyEngineering

Abstract

fetched live from OpenAlex

Performing a surgical task on a beating heart requires superhuman skill as the surgeon must manually track the heart’s motion while performing a surgical task. However, the ability to operate on a beating heart would eliminate the need to use a mechanical stabilizer or arrest the heart and connect the patient to a heart-lung machine and would consequently eliminate their side effects. This work develops the image processing and control structure for an ultrasound-guided robot-assisted beating heart surgical system that will move the surgical tool tip in synchrony with the heart. This would allow the surgeon to operate through teleoperation on a virtually stabilized point on the heart. In developing this system, the position data acquired from ultrasound images is upsampled and predicted ahead to compensate for the image acquisition and processing delay. We present the results of a user task based on mitral valve annuloplasty performed under ultrasound guidance.

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.

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)0.005
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
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 head0.067
GPT teacher head0.362
Teacher spread0.295 · 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