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Guangkun Nie on Muck Rack

Guangkun Nie

As seen in: Nature
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Articles

Fine-tuning an ECG foundation model to predict coronary CT angiography outcomes

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By Yujie Xiao, Qinghao Zhao, Hao Zhang, Deyun Zhang, Jun Li, Guangkun Nie, Haoyu Wang, Shun Huang, tong liu, Jian Liu, Kangyin Chen, Shenda Hong, Gongzheng Tang, Zhuoran Kan, Xiaocheng Fang
| Nature Verified
Abstract Coronary artery disease (CAD) remains a major global public health burden, yet scalable pre-imaging risk stratification tools are limited. In this multicenter study, we developed and validated an artificial intelligence-enabled electrocardiography (AI-ECG) model using coronary computed tomographic angiography (CCTA) as the anatomical reference to predict vessel-specific hemodynamically significant stenosis (≥70% for RCA, LAD, LCX; ≥50% for LM).
Open in Who Shared Issue with article?

Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health - Communications Medicine

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By Guangkun Nie
| Nature Verified
Abstract Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health.
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