Coronary Angiography
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Last Posted: May 28, 2024
- Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.
Andreas Leiherer et al. Int J Mol Sci 2024 25(10) - Clinical and genetic diagnosis of familial hypercholesterolaemia in patients undergoing coronary angiography: the Ludwigshafen Risk and Cardiovascular Health Study.
Stefan Molnar et al. Eur Heart J Qual Care Clin Outcomes 2024 - 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 - The use of artificial intelligence in interventional cardiology.
Hakan Göçer et al. Turk Gogus Kalp Damar Cerrahisi Derg 2023 31(3) 420-421 - Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.
Yinghui Meng et al. Technol Health Care 2023 - 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 - 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 - 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 - 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 - Machine learning-enhanced echocardiography for screening coronary artery disease.
Ying Guo et al. Biomed Eng Online 2023 22(1) 44 - 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 - 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 - Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.
Zengfa Huang et al. Acta radiologica (Stockholm, Sweden : 1987) 2023 2841851231158730 - Role of an automated screening tool for familial hypercholesterolemia in patients with premature coronary artery disease.
Jokiniitty Antti et al. Atherosclerosis plus 2023 481-7 - 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 - 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 - 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 - 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 - Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD.
D'Ancona Giuseppe et al. International journal of cardiology 2022 - Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.
Alskaf Ebraham et al. Informatics in medicine unlocked 2022 32101055 - 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 - 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 - Usefullness of MicroRNAs in Predicting the Clinical Outcome of Patients with Acute Myocardial Infarction During Follow-Up: A Systematic Review.
Venugopal Priyanka et al. Genetic testing and molecular biomarkers 2022 26(5) 277-289 - 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 - 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 - Association of Lipoprotein (a) in Coronary Artery Disease in Young Individuals.
Patted Aishwarya et al. The Journal of the Association of Physicians of India 2022 70(4) 11-12 - "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 - 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 - 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 - [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 - 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 - 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 - 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 - Machine learning of treadmill exercise test to improve selection for testing for coronary artery disease.
Lee Yin-Hao et al. Atherosclerosis 2021 34023-27 - 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 - Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.
Xu Lixue et al. Frontiers in cardiovascular medicine 2021 8707508 - Diagnostic Improvements of Deep Learning-Based Image Reconstruction for Assessing Calcification-Related Obstructive Coronary Artery Disease.
Yi Yan et al. Frontiers in cardiovascular medicine 2021 8758793 - Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization.
Gudigar Anjan et al. International journal of environmental research and public health 2021 18(19) - Familial Hypercholesterolemia Genetic Variations and Long-Term Cardiovascular Outcomes in Patients with Hypercholesterolemia Who Underwent Coronary Angiography.
Lee Wen-Jane et al. Genes 2021 12(9) - Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.
Chiu Wei-Ting et al. Journal of the Formosan Medical Association = Taiwan yi zhi 2021
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HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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- Page last updated:Jun 28, 2024
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