A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease

2010 ◽  
pp. 18-26 ◽  
Author(s):  
İsmail Babaoğlu ◽  
Ömer Kaan Baykan ◽  
Nazif Aygül ◽  
Kurtuluş Özdemir ◽  
Mehmet Bayrak
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Artificial Intelligence Methods ◽  
Artery Disease

scholarly journals The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping

Circulation ◽  
2020 ◽  
Author(s):  
Kristopher D. Knott ◽  
Andreas Seraphim ◽  
Joao B. Augusto ◽  
Hui Xue ◽  
Liza Chacko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Myocardial Perfusion Reserve ◽  
Prognostic Significance ◽  
Suspected Coronary Artery Disease ◽  
Major Adverse Cardiovascular Events ◽  
Co Morbidity ◽  
Artery Disease

Background: Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF). Methods: A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (>90 day) revascularization and death. Results: 1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, P<0.0001) respectively, even after adjusting for age and co-morbidity. For each 1 unit decrease in MPR the adjusted HR for death and MACE were 2.45 (95% CI 1.42-4.24, P=0.001) and 1.74 (95% CI 1.36-2.22, P<0.0001) respectively. In patients without regional perfusion defects on clinical read and no known macrovascular coronary artery disease (n=783), MPR remained independently associated with death and MACE, with stress MBF remaining associated with MACE only. Conclusions: In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

scholarly journals Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

2021 ◽  
Vol 8 ◽  
Author(s):  
Riccardo Maragna ◽  
Carlo Maria Giacari ◽  
Marco Guglielmo ◽  
Andrea Baggiano ◽  
Laura Fusini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Multimodality Imaging ◽  
Nuclear Imaging ◽  
Comprehensive Overview ◽  
Early Predictors ◽  
New Frontier ◽  
Artery Disease ◽  
Human Eyes

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

scholarly journals High-performance Cardiovascular Medicine: Artificial Intelligence for Coronary Artery Disease

2021 ◽  
Author(s):  
H.M.K.K.M.B. Herath ◽  
G.M.K.B. Karunasena ◽  
H.D.N.S. Priyankara ◽  
B.G.D.A. Madhusanka
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Disease ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Performance ◽  
Alcohol Intake ◽  
World Health ◽  
Essential Factor ◽  
Artery Disease ◽  
Health Organization

Abstract Cardiovascular disease (CVD) is identified as the leading cause of death globally, according to the World Health Organization (WHO). Approximately 17.9 million people are dying due to cardiovascular disease, which is an estimation of 31% of all deaths worldwide. CVDs are generally affecting the heart and blood vessels in the human body. Since healthcare is an essential factor for a country and its economy, researchers are looking for solutions to predict disease before getting into serious problems. This research introduces a method to development of an algorithm to predict coronary artery disease based on artificial intelligence. The algorithm was tested with 72 random subjects, which covered 11 attributes such as age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, glucose, smoking, alcohol intake, and physical activities. According to the results, the prediction accuracy of the system was 81.62% at 0.879 precision.

An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease

2021 ◽  
Author(s):  
Pang-Shuo Huang ◽  
Yu-Heng Tseng ◽  
Jien-Jiun Chen ◽  
Shao-Chi Yang ◽  
Fu-Chun Chiu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Artery Disease

P5245Cost-saving diagnosis approach by artificial intelligence tool in patients with suspected coronary artery disease. The co-operative ARTICA registry database

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Mazzanti ◽  
E Shirka ◽  
H Gjergo ◽  
F Pugliese ◽  
A Goda
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Cost Saving ◽  
Suspected Coronary Artery Disease ◽  
Model Parameters ◽  
Exercise Treadmill ◽  
Artery Disease ◽  
Stable Chest Pain

Abstract Background Although coronary tomographic angiography (CTA) has shown promise as a “gatekeeper” to invasive coronary angiography (ICA) in longitudinal cohort studies, it remains unknown whether the strategy of direct initial performance of CTA is cost-effective when compared with selected exercise treadmill testing (ETT) +/− functional cardiac imaging strategies in patients with suspected coronary artery disease (CAD). An innovative artificial intelligence (AI) Decision Support System (DSS) ESC guidelines based has been used at point of care for evaluating subjects with stable chest pain (SCP). Purpose The objective was to verify the cost-saving effect of the robotic AI DSS vs direct CTA by human standard care (SD) for diagnosing CAD in subjects presenting with SCP. Methods From October 2016 over three hospitals, 1017 subjects, 620 males, age 62±11 years, with clinically SCP being referred for CTA by SD received also a same day pre-scan AI DSS administration. All patients did not demonstrate significant CAD at CTA. CTA/ICA, or exercise treadmill test (ETT)/ stress echocardiography (SE), gated myocardial perfusion scintigraphy (gMPS) or Follow up/No tests (FNT) strategies by AI DSS were analyzed and compared to direct CTA SD. Pre-test likelihood (pt-lk) of CAD consider clinical risk factors into the model. Sensitivity and specificity of non-invasive diagnostic tests within our model were based upon a bivariate analysis of data from published multicenter trials. Costs of procedures were calculated by the sum of technical and professional components. Probabilistic sensitivity analysis was conducted to assess the impact of uncertainty in model parameters. Results The direct approach used performing direct CTA strategy by SD in all subjects costed 406.800 €. Costs of each procedure and distribution of AI DSS outputs are shown in the Table. Across the range of pt-lk of CAD, total costs of AI DSS strategy resulted 146.030€ with −65% vs SD approach. AI DSS tests distribution and costs pt-lk (pt/%) FNT (0€) ETT (90€) SE (350€) Stress gated MPS (750€) CCTA (400€) ICA (3.000€) High (29/2.8) 0 0 1 2 0 26 Int (371/36.5) 259 5 51 48 7 1 Low (612/60.7) 595 2 2 0 13 0 Total costs (€) 0 630 18,900.00 37,500.00 8,000.00 81,000.00 Conclusion These results from ARTICA registry seem to demonstrate that AI DSS is extremely cost-saving in subjects with stable chest pain across the whole range of pt-lk of CAD.

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