biostudies-literatureUnknown24(1)Chen WW<h4>Background</h4>Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows.<h4>Methods</h4>The proposed method introduces a novel approach that integrates clinical data and electrocardiograms (ECGs) using a colorization technique. This technique recolors ECG images based on patients' demographic information while preserving their original characteristics and incorporating color correlations from statistical data features. Our primary objective is to enhance atrial fibrillation (AF) detection by fusing ECG images with demogBMC medical research methodology318https://www.ebi.ac.uk/biostudies/studies/S-EPMC11665121biostudies-literatureIdentifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.PMC11665121Huang CCWu ICWu JCKuo LTseng CCChen WWLo LWLiao JNLin YJChang TYHu YFLin CYLiu SHChen PFLu HHWu CIChung FPTuan TCChen SAChao TFChang SLLiu CMfalseIdentifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.<h4>Background</h4>Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows.<h4>Methods</h4>The proposed method introduces a novel approach that integrates clinical data and electrocardiograms (ECGs) using a colorization technique. This technique recolors ECG images based on patients' demographic information while preserving their original characteristics and incorporating color correlations from statistical data features. Our primary objective is to enhance atrial fibrillation (AF) detection by fusing ECG images with demog2024-01-01T00:00:00Z2024 Dec2025-04-04T20:28:52.502Z2025-04-04T20:28:52.502ZS-EPMC116651213971606410.1186/s12874-024-02421-0