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Machine-learning based prediction of obstructive coronary artery disease using integrated submodules of clinical information, chest x-ray, and electrocardiography

30 August 2024 (08:00 - 17:30)
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Congress Presentation Part of: Non-invasive methods for evaluating coronary artery disease Noninvasive Diagnostic Methods ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

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Professor Ran Heo

Hanyang university medical center, Seoul (Korea (Republic of))

7 More presentations in this session

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Non-invasive imaging to guide percutaneous coronary revascularization in chronic total occlusion: a systematic review and meta-analysis
Doctor G. Bisaccia (Chieti, IT)
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Enhancing computational fluid dynamics simulation for coronary artery disease analysis through spatial iterative modeling
Mr M. Arya (Izmir, TR)
Abstract Slides Video
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Quantitative myocardial blood flow distribution derived from CCTA and determination of myocardium at risk in the FAST TRACK CABG trial
Professor P. Serruys (Galway, IE)
Abstract Slides Video
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Prognostic implication of computational angiography-derived fractional flow reserve in patients with non-obstructive coronary artery disease
Mr Y. Hung (Hong Kong, HK)
Abstract Slides Video
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angioPy: an open-source, deep learning-driven tool for coronary artery segmentation
Doctor T. Mahendiran (Aalst, BE)
Abstract Slides Video

Access the full session

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Non-invasive methods for evaluating coronary artery disease

The Event

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ESC Congress 2024

30 August - 2 September 2024
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ESC 365 is supported by

novartis Explore sponsored resources