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Artificial intelligence for advanced analysis of coronary plaque.
van Assen, Marly; von Knebel Doeberitz, Philipp; Quyyumi, Arshed A; De Cecco, Carlo N.
Afiliação
  • van Assen M; Department of Radiology and Imaging Sciences, Emory University, Inc. 1365 Clifton Road NE, Suite-AT503, Atlanta, GA 30322, USA.
  • von Knebel Doeberitz P; Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA 30322, USA.
  • Quyyumi AA; Department of Radiology and Imaging Sciences, Emory University, Inc. 1365 Clifton Road NE, Suite-AT503, Atlanta, GA 30322, USA.
  • De Cecco CN; Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA 30322, USA.
Eur Heart J Suppl ; 25(Suppl C): C112-C117, 2023 May.
Article em En | MEDLINE | ID: mdl-37125298
ABSTRACT
The field of coronary plaque analysis is advancing including more quantitative analysis of coronary artery diseases such as plaque burden, high-risk plaque features, computed tomography-derived fractional flow reserve, and radiomics. Although these biomarkers have shown great promise for the diagnosis and prognosis of cardiac patients in a research setting, many of these advanced analyses are labour and time intensive and therefore hard to implement in daily clinical practice. Artificial intelligence (AI) is playing an increasing role in supporting the quantification of these new biomarkers. AI offers the opportunity to increase efficiency, reduce human error and reader variability and to increase the accuracy of diagnosis and prognosis by automating many processing and supporting clinicians in their decision-making. With the use of AI these novel analysis approaches for coronary artery disease can be made feasible for clinical practice without increasing cost and workload and potentially improve patient care.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article