AINeutralarXiv – CS AI · Feb 274/105
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Learning geometry-dependent lead-field operators for forward ECG modeling
Researchers developed a new AI-powered surrogate model for ECG simulations that combines geometry encoding with neural networks to predict lead-field gradients. The method achieves high accuracy (5° mean angular error, <2.5% relative error) while reducing computational costs and data requirements compared to traditional full-order models.