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Record W2027721693 · doi:10.2174/1568006043336311

3D Ultrasound Imaging of the Carotid Arteries

2004· review· en· W2027721693 on OpenAlexaff
Aaron Fenster, Anthony Landry, Dónal B. Downey, Robert A. Hegele, J. David Spence

Bibliographic record

VenueCurrent Drug Targets - Cardiovascular & Hematological Disorders · 2004
Typereview
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsUltrasound3D ultrasoundModality (human–computer interaction)Carotid arteriesMedicineUltrasonographyRadiologyModalitiesVolume (thermodynamics)Power dopplerComputer scienceBiomedical engineeringArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and reproducible monitoring of carotid disease and plaque burden. 2D viewing of 3D anatomy, using conventional ultrasonography limits our ability to quantify and visualize carotid disease and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques that are capable of acquiring B-mode, color Doppler and power Doppler images of the carotid arteries using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images of the carotid arteries has allowed the development of techniques to quantify plaque volume and surface morphology as well as allowing registration with other 3D imaging modalities. This paper describes 3D ultrasound imaging techniques used to image the carotid arteries and summarizes some of the developments aimed at quantifying plaque volume and morphology.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.015
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.280
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2004
Admission routes1
Has abstractyes

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Same venueCurrent Drug Targets - Cardiovascular & Hematological DisordersSame topicCerebrovascular and Carotid Artery DiseasesFrench-language works237,207