Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BACKGROUND: Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard d...

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Enregistré dans:
Détails bibliographiques
Publié dans:Biomed Eng Online
Auteurs principaux: Guo, Xiaoya, Maehara, Akiko, Matsumura, Mitsuaki, Wang, Liang, Zheng, Jie, Samady, Habib, Mintz, Gary S., Giddens, Don P., Tang, Dalin
Format: Artigo
Langue:Inglês
Publié: BioMed Central 2021
Sujets:
Research
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC8025351/
https://ncbi.nlm.nih.gov/pubmed/33823858
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12938-021-00868-6
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