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Record W2148558227 · doi:10.1109/titb.2008.926496

A Framework for the Design of a Novel Haptic-Based Medical Training Simulator

2008· article· en· W2148558227 on OpenAlexaff
A.M. Tahmasebi, Keyvan Hashtrudi-Zaad, D Thompson, Purang Abolmaesumi

Bibliographic record

VenueIEEE Transactions on Information Technology in Biomedicine · 2008
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsQueen's University
Fundersnot available
KeywordsHaptic technologyKinesthetic learningComputer scienceModular designMedical simulationImaging phantomSimulationSoftwareComputer architecture simulatorUltrasoundMedical physicsRadiologyMedicine

Abstract

fetched live from OpenAlex

This paper presents a framework for the design of a haptic-based medical ultrasound training simulator. The proposed simulator is composed of a PHANToM haptic device and a modular software package that allows for visual feedback and kinesthetic interactions between an operator and multimodality image databases. The system provides real-time ultrasound images in the same fashion as a typical ultrasound machine, enhanced with corresponding augmented computerized tomographic (CT) and/or MRI images. The proposed 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. Low-level details of the software structure that can be migrated to other similar medical simulators are described. A preliminary human factors study, conducted on the prototype of the developed simulator, demonstrates the potential usage of the system for clinical training.

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 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 score0.955
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.327
Teacher spread0.253 · 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
GenreMethods

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

Citations25
Published2008
Admission routes1
Has abstractyes

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