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Record W2462596504 · doi:10.1177/154431671303700204

Carotid Artery Disease Imaging: A Home-Produced, Easily Made Phantom for Two- and Three-Dimensional Ultrasound Simulation

2013· article· en· W2462596504 on OpenAlexaff
Lysa Legault Kingstone, Marc Castonguay, Carlos Torres, Geoffrey Currie

Bibliographic record

VenueJournal for Vascular Ultrasound · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsImaging phantomUltrasoundCarotid arteriesMedicineCarotid artery diseaseRadiologyUltrasound imagingBiomedical engineeringMedical physicsCardiology

Abstract

fetched live from OpenAlex

Background and Purpose —Ultrasound (US) plaque characterization has great potential with regard to maximizing the information traditionally gathered with spectral Doppler examination. It can directly visualize plaque and quantify better features such as surface morphology, geometry, volume, and echotexture via B-mode and the three-dimensional (3D) imaging mechanism. One of the major pitfalls of carotid imaging is the use of freehand manual manipulation. The application of angling, steering, as well as variability in the technical parameters, can increase the interobservation inconsistency. A limited number of commercial phantoms are available to teach this advanced technique but come at a high cost. We developed a home-produced phantom model to practice and teach carotid atherosclerotic disease imaging. We also investigated interobservation variability using two-dimensional (2D) characterization and 3D mechanical planimetry. This study presents a recipe to create an ultrasonic phantom that simulates a diseased carotid artery segment and how it can be used in identifying the 2D and 3D US interobservation variability. Methods —We created five tissue-like phantoms to simulate various types of diseased plaque segments. To simulate the plaque, a piece of frankfurter was cut and detailed to represent various forms of diseased plaque. Each mould contained dissimilar types of mimicked-plaque, including a soft-plaque, fissured, ulcerated, irregular surfaced, and calcified segment. We used a mixture of gelatin and Metamucil to mimic a previously published soft-tissue mixture. To create a vessel, we used a powder-free, nitrile examination glove. The frankfurter was held in place inside the middle finger of the glove using adhesive gel and filled with mineral oil. Preparation included interval refrigeration of the concoction of the mould. Trained sonographers imaged the plaque using a linear small parts probe for 2D and a mechanical 3D probe for 3D US. Two neuroradiologists assessed the corresponding images and reported their findings including the internal plaque contents, volume, and geometry. Analysis was performed on the inter-observation and inter-reading variability. Results —Interobserver and interreader reliabilities were high, and plaque volume measurement variability decreased with increasing plaque volume. There was increased sensitivity and specificity for each plaque phantom with the use of 3D versus 2D alone. Neuroradiologists reports were 96% sensitive and 97% specific, respectively, when they used combined 2D and 3D US. Conclusion —We created a 2D and 3D vascular US carotid phantom. This phantom is an excellent educational tool to simulate various degrees of diseased carotid segments; moreover, it can be made easily and inexpensively and is reusable. This phantom represents the vessel anatomy and pathology extremely well. We implemented a standardized scanning protocol and created a plaque morphological worksheet to cover all plaque characterization criteria and achieve optimal imaging. Results indicated minimal interobservation and interreader variability. Additional studies are required to address the phantom's longevity and whether or not it can improve the sonographer's skills.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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.

Study designObservational
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

Citations7
Published2013
Admission routes1
Has abstractyes

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