AI RESEARCH
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
arXiv CS.LG
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ArXi:2604.27017v1 Announce Type: cross Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature attribution methods are limited by the inherent difficulty in mapping abstract waveform fluctuations to physical anatomical pathologies. To resolve this, we propose a cross-modal method that projects feature attributions from high-performance 12-lead ECG models onto the CineECG 3D anatomical space.