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Record W2538075778 · doi:10.1109/iembs.2004.1403551

A haptic-based system for medical image examination

2005· article· en· W2538075778 on OpenAlexafffund
Purang Abolmaesumi, Keyvan Hashtrudi-Zaad, D Thompson, A.M. Tahmasebi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill UniversityQueen's University
FundersCanadian Space Agency
KeywordsHaptic technologyComputer scienceInterface (matter)Medical simulationComputer visionMedical physicsPosition (finance)Artificial intelligenceVirtual realityMedical imagingSimulationVirtual imageOperator (biology)Virtual patientHuman–computer interactionComputer graphics (images)Medicine

Abstract

fetched live from OpenAlex

This paper presents a haptic-based simulator for training of radiology residents and sonographers. The system consists of a force feedback haptic device providing means to interact in real-time with volumetric images of a virtual patient, captured pre-operatively from several subjects. The training system allows trainees to develop radiology techniques and knowledge of the patient's anatomy with minimum practice on live patients, or in places or at times when radiology devices or patients with rare cases may not be available. The haptic interface guarantees position correspondence between the operator's hand and a virtual probe position that slices medical volume sets in the plane of the probe. Thus the simulated procedure becomes nearly identical to the real examinations at the hospital. Different configurations of the system are implemented and presented. Future potential applications for the system are discussed as well.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.366

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 head0.009
GPT teacher head0.221
Teacher spread0.213 · 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

Citations9
Published2005
Admission routes2
Has abstractyes

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