Model development and validation of noninvasive parameters based on coronary computed tomography angiography to predict culprit lesions in acute coronary syndromes within 3 years: value of plaque characteristics, hemodynamics and pericoronary adipose tissue

BACKGROUND: Machine learning (ML) is combined with noninvasive parameters from coronary computed tomography angiography (CTA) to construct predictive models to identify culprit lesions that may lead to acute coronary syndrome (ACS). METHODS: We retrospectively analyzed 132 patients with ACS at the F...

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Detalles Bibliográficos
Autores principales: Li, Na, Dong, Xiaolin, Zhu, Chentao, Shi, Ke, Si, Nuo, Shi, Zhenzhou, Pan, Hong, Wang, Shuting, Zhao, Min, Zhang, Tong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347311/
https://www.ncbi.nlm.nih.gov/pubmed/37456302
http://dx.doi.org/10.21037/qims-22-1045

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347311/
https://www.ncbi.nlm.nih.gov/pubmed/37456302
http://dx.doi.org/10.21037/qims-22-1045

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