DeepHeartCT: A Fully Automatic Artificial Intelligence System For Cardiac Computed Tomography Angiography Multi-Structure Image Segmentation

2021 ◽  
Vol 15 (4) ◽  
pp. S4
Author(s):  
V. Bui ◽  
L. Hsu ◽  
L. Tran ◽  
S. Shanbhag ◽  
L. Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Segmentation ◽  
Computed Tomography Angiography ◽  
Cardiac Computed Tomography ◽  
Artificial Intelligence System ◽  
Cardiac Computed Tomography Angiography ◽  
Intelligence System ◽  
Fully Automatic

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

2021 ◽  
Author(s):  
Teresa Infante ◽  
Carlo Cavaliere ◽  
Bruna Punzo ◽  
Vincenzo Grimaldi ◽  
Marco Salvatore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Computed Tomography Angiography ◽  
Cardiac Computed Tomography ◽  
Imaging Features ◽  
Cardiac Computed Tomography Angiography

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.

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.

Spontaneous coronary artery dissection: how often do we miss this diagnosis? The role of cardiac computed tomography angiography

2019 ◽  
Vol 15 (5) ◽  
pp. 333-338 ◽  
Author(s):  
Giuliana Capretti ◽  
Satoru Mitomo ◽  
Manuela Giglio ◽  
Antonio Colombo ◽  
Alaide Chieffo
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Young Women ◽  
Computed Tomography Angiography ◽  
Cardiac Computed Tomography ◽  
Artery Dissection ◽  
Spontaneous Coronary Artery Dissection ◽  
Coronary Artery Dissection ◽  
Cardiac Computed Tomography Angiography

Spontaneous coronary artery dissection (SCAD) is an important cause of acute coronary syndrome particularly among young women. Although coronary angiogram (CAG) is the gold standard exam for the diagnosis, SCAD may be missed by CAG alone. Our case series illustrates the adjunctive role of cardiac computed tomography angiography (cCTA) to CAG in ascertaining the diagnosis of SCAD. Three young women were admitted with ST-segment elevation myocardial infarction. CAG showed no significant coronary artery stenosis. In two patients, cCTA performed after CAG revealed an intramural hematoma compressing the coronary lumen. In one patient, SCAD was initially misdiagnosed as Takotsubo cardiomyopathy and cCTA performed 1 month later allowed to make the correct diagnosis of SCAD assessing the spontaneous healing of the dissected vessel.

scholarly journals Bailout intravenous esmolol for heart rate control in cardiac computed tomography angiography

2016 ◽  
Vol 35 (12) ◽  
pp. 673-678 ◽  
Author(s):  
Sílvia Aguiar Rosa ◽  
Ruben Ramos ◽  
Hugo Marques ◽  
Rosana Santos ◽  
Cecília Leal ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Heart Rate ◽  
Computed Tomography Angiography ◽  
Rate Control ◽  
Cardiac Computed Tomography ◽  
Heart Rate Control ◽  
Cardiac Computed Tomography Angiography
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