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
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About HLBS-PopOmics
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 21, 2024
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