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Record W2009912861 · doi:10.1002/rob.10043

Application of visual tracking for robot‐assisted laparoscopic surgery

2002· article· en· W2009912861 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.

Bibliographic record

VenueJournal of Robotic Systems · 2002
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEndoscopeLaparoscopic surgeryInvasive surgeryTracking (education)Computer visionDistortion (music)Artificial intelligenceRobotic surgeryComputer scienceManipulator (device)RobotSurgical robotEngineeringSurgeryMedicineLaparoscopyPsychology

Abstract

fetched live from OpenAlex

Abstract With the increasing popularity of laparoscopic surgery, the demand for better modes of laparoscopic surgery also increases. The current laparoscopic surgery mode requires an assistant to hold and manipulate the endoscope through commands from the surgeon. However, during lengthy surgery procedures, accurate and on‐time adjustment of the camera cannot be guaranteed due to the fatigue and hand trembling of the camera assistant. This article proposes a practical visual tracking method to achieve automated instrument localization and endoscope maneuvering in robot‐assisted laparoscopic surgery. Solutions concerning this approach, such as, endoscope calibration, marker design, distortion correction, and endoscope manipulator design are described in detail. Experimental results are presented to show the feasibility of the proposed method. © 2002 Wiley Periodicals, Inc.

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)0.000
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 score0.956
Threshold uncertainty score0.447

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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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