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Qualitative and quantitative assessment of deep learning-based raw data motion correction in atrial fibrillation: benefit in high heart rate and motion-prone segments

30 August 2026 (14:15 - 15:00)
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Congress Presentation Part of: Artificial intelligence-enhanced cardiac computed tomography Image Reconstruction ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Associate Professor Hideki Kawai

Fujita Health University, Toyoake (Japan)

7 More presentations in this session

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Machine learning CT-FFR for physiologic decision-making in diffuse coronary artery disease
Doctor Z. An (Beijing, CN)
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FFR vs iFR for clinical outcomes: insights from machine-learning risk stratification
Doctor D. Ferreira (Lisbon, PT)
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Predictive value of serial coronary CTA-derived fractional flow reserve in patients with coronary artery disease
Doctor M. Zhang (Beijing, CN)
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Machine learning–based coronary plaque assessment across imaging modalities: a systematic review and meta-analysis of physiologically validated studies
Mr E. Ginghina (Latina, IT)
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Semi-quantitative coronary computed tomography angiography plaque scores predict adverse cardiovascular outcomes: a systematic review and meta-analysis
Mr G. Mann (Sydney, AU)
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Artificial intelligence-enhanced cardiac computed tomography

The Event

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ESC Congress 2026

30 August 2026 14:15 CET
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