scholarly journals Cardiac risk factors and risk scores vs cardiac computed tomography angiography: a prospective cohort study for triage of ED patients with acute chest pain

2013 ◽  
Vol 31 (10) ◽  
pp. 1479-1485 ◽  
Author(s):  
Ethan J. Halpern ◽  
Jacob P. Deutsch ◽  
Maria M. Hannaway ◽  
Adrian T. Estepa ◽  
Anand S. Kenia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Computed Tomography ◽  
Cohort Study ◽  
Chest Pain ◽  
Computed Tomography Angiography ◽  
Cardiac Computed Tomography ◽  
Acute Chest Pain ◽  
Cardiac Risk ◽  
Risk Scores ◽  
Cardiac Computed Tomography Angiography

scholarly journals Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Giuseppe Muscogiuri ◽  
Marly Van Assen ◽  
Christian Tesche ◽  
Carlo N. De Cecco ◽  
Mattia Chiesa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Cardiac Computed Tomography ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Risk Scores ◽  
Cardiac Computed Tomography Angiography ◽  
Clinical Scenarios ◽  
Artery Disease

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.

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