Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.

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Tác giả: Martin Bishop, Ludovica Cicci, Yu Deng, Elliot Fairweather, Brian P Halliday, Daniel Hammersley, Richard E Jones, Pablo Lamata, Xingchi Liu, Steven Niederer, Gernot Plank, Sanjay Prasad, Shuang Qian, Reza Razavi, Marina Strocchi, Laura Dal Toso, Devran Ugurlu, Edward Vigmond, Alistair Young, Hassan Zaidi

Ngôn ngữ: eng

Ký hiệu phân loại: 978.02 1800–1899

Thông tin xuất bản: England : Nature cardiovascular research , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 746224

Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncovering multi-scale insights tied to these mechanisms. In this study, we constructed 3,461 CDTs from the UK Biobank and another 359 from an ischemic heart disease (IHD) cohort, using cardiac magnetic resonance images and electrocardiograms. We show here that sex-specific differences in QRS duration were fully explained by myocardial anatomy while their myocardial conduction velocity (CV) remains similar across sexes but changes with age and obesity, indicating myocardial tissue remodeling. Longer QTc intervals in obese females were attributed to larger delayed rectifier potassium conductance
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