1 citations · 1 across the 15 of their papers we have counts for
17 papers
Smartwatch Photoplethysmography-Derived Heart Age via ECG-Guided Cross-Modal Pretraining as a Digital Biomarker of Vascular Aging
Donglin Xie, Xueying Gui, Yutian Zhu +9
Digital biomarkers of cardiovascular aging, often termed heart or vascular age, have been widely studied, but most rely on resting electrocardiography (ECG), imaging, or specialize…
EchoBridge: Long-Tail-Aware ECG-Echocardiography Text Alignment for Echocardiography-Derived Cardiac Findings
Xiaocheng Fang, Jieyi Cai, Guangkun Nie +9
Standardized echocardiography conclusions provide meaningful supervision for learning ECG representations of echocardiography-derived cardiac findings. Global ECG--text alignment m…
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
Xiaocheng Fang, Haoyu Wang, Jieyi Cai +14
Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG imag…
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Bo Liu, Di Dai, Jingwei Liu +5
Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-al…
Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
Donglin Xie, Qingshuo Zhao, Jingyu Wang +9
Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night…
Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment
Gongzheng Tang, Qinghao Zhao, Guangkun Nie +10
Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitoring remains difficult outside…
AnyECG: Evolved ECG Foundation Model for Holistic Health Profiling
Jun Li, Hongling Zhu, Yujie Xiao +8
Background: Artificial intelligence enabled electrocardiography (AI-ECG) has demonstrated the ability to detect diverse pathologies, but most existing models focus on single diseas…
ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction
Xiaocheng Fang, Zhengyao Ding, Guangkun Nie +9
Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high acquisition costs and reliance on exp…
Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes
Yujie Xiao, Qinghao Zhao, Gongzheng Tang +12
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 a…
AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling
Guangkun Nie, Xiaocheng Fang, Gongzheng Tang +5
Photoplethysmography (PPG) is widely used as a non-invasive and accessible modality for continuous health monitoring. However, despite being a peripheral hemodynamic signal intrins…
AnyECG-Lab: An Exploration Study of Fine-tuning an ECG Foundation Model to Estimate Laboratory Values from Single-Lead ECG Signals
Yujie Xiao, Gongzhen Tang, Wenhui Liu +6
Timely access to laboratory values is critical for clinical decision-making, yet current approaches rely on invasive venous sampling and are intrinsically delayed. Electrocardiogra…
Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients
Shun Huang, Wenlu Xing, Shijia Geng +5
Malignant ventricular arrhythmias (VT/VF) following acute myocardial infarction (AMI) are a major cause of in-hospital death, yet early identification remains a clinical challenge.…
PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection
Xiaocheng Fang, Jiarui Jin, Haoyu Wang +8
Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the need for dedicated hardware and…
Artificial Intelligence-derived Photoplethysmography Age as a Digital Biomarker for Cardiovascular Health
Guangkun Nie, Qinghao Zhao, Gongzheng Tang +2
Background: Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce…
Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
Guangkun Nie, Gongzheng Tang, Shenda Hong
Imbalanced data distributions are prevalent in real-world scenarios, posing significant challenges in both imbalanced classification and imbalanced regression tasks. They often cau…
Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
Guangkun Nie, Qinghao Zhao, Gongzheng Tang +2
Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep…
A Scoping Review of Deep Learning Methods for Photoplethysmography Data
Guangkun Nie, Jiabao Zhu, Gongzheng Tang +4
Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad deployment in both clinical monitoring…