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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis.
Dan-Ying Lee et al. Eur J Clin Invest 2023 e14089
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The use of artificial intelligence in interventional cardiology.
Hakan Göçer et al. Turk Gogus Kalp Damar Cerrahisi Derg 2023 31(3) 420-421
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Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.
Yinghui Meng et al. Technol Health Care 2023
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Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.
Qi Han et al. Quant Imaging Med Surg 2023 13(6) 3789-3801
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Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.
Seong Huan Choi et al. BMC Cardiovasc Disord 2023 23(1) 287
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A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction.
Shantanu Sengupta et al. Eur Heart J Digit Health 2023 4(3) 145-154
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Pre-test probability for coronary artery disease in patients with chest pain based on machine learning techniques.
Byoung Geol Choi et al. Int J Cardiol 2023
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Machine learning-enhanced echocardiography for screening coronary artery disease.
Ying Guo et al. Biomed Eng Online 2023 22(1) 44
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Accuracy of Artificial Intelligence-Based Automated Quantitative Coronary Angiography Compared to Intravascular Ultrasound: Retrospective Cohort Study.
In Tae Moon et al. JMIR Cardio 2023 7e45299
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A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.
Valeria Raparelli et al. Clinical research in cardiology : official journal of the German Cardiac Society 2023
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Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.
Zengfa Huang et al. Acta radiologica (Stockholm, Sweden : 1987) 2023 2841851231158730
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Development and evaluation of a radiomics model of resting N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease.
Zhang Xiaochun et al. Annals of translational medicine 2022 10(21) 1167
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A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease.
Yan Jingjing et al. Computers in biology and medicine 2022 151(Pt A) 106300
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Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study.
Cao Jiaoyu et al. PeerJ 2022 10e14078
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Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.
Hagio Tomoe et al. European journal of nuclear medicine and molecular imaging 2022
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Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD.
D'Ancona Giuseppe et al. International journal of cardiology 2022
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Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.
Alskaf Ebraham et al. Informatics in medicine unlocked 2022 32101055
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Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.
Johri Amer M et al. Computers in biology and medicine 2022 150106018
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Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks.
Huang Zengfa et al. Academic radiology 2022
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Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.
Kwon Osung et al. JMIR medical informatics 2022 10(5) e26801
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Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.
Miller Robert J H et al. Journal of nuclear medicine : official publication, Society of Nuclear Medicine 2022
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"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.
Hagio Tomoe et al. European journal of nuclear medicine and molecular imaging 2022
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Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach.
Li Yu-Hsuan et al. Frontiers in cardiovascular medicine 2022 9800864
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A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms.
Schwalm J D et al. Cardiovascular digital health journal 2022 3(1) 21-30
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AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.
Griffin William F et al. JACC. Cardiovascular imaging 2022
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[Development of the DSA Method for Coronary Angiography Using Deep Learning Techniques].
Yamamoto Megumi et al. Nihon Hoshasen Gijutsu Gakkai zasshi 2022 78(2) 129-139
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Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.
Han Xianjun et al. BMC medical imaging 2022 22(1) 28
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Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study.
Zhang Chun-Ke et al. BioMed research international 2022 20215047501
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Machine learning of treadmill exercise test to improve selection for testing for coronary artery disease.
Lee Yin-Hao et al. Atherosclerosis 2021 34023-27
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Combined Coronary CT-Angiography and TAVR Planning for Ruling Out Significant Coronary Artery Disease: Added Value of Machine-Learning-Based CT-FFR.
Gohmann Robin F et al. JACC. Cardiovascular imaging 2021
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Feb 1, 2024
Page last updated:
Apr 25, 2024
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Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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