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Enregistrement 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 sur OpenAlex
Lysa Legault Kingstone, Marc Castonguay, Carlos Torres, Geoffrey Currie

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueJournal for Vascular Ultrasound · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueCerebrovascular and Carotid Artery Diseases
Établissements canadiensOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésImaging phantomUltrasoundCarotid arteriesMedicineCarotid artery diseaseRadiologyUltrasound imagingBiomedical engineeringMedical physicsCardiology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,100
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,271
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle