ECGEchoDiag: A Benchmark for ECG-Conditioned Multi-View Echocardiography Generation and Cardiac Disease Recognition

Electrocardiography (ECG) enables prolonged cardiac monitoring but cannot directly visualize cardiac anatomy or motion. ECG-conditioned echocardiography (Echo) generation offers a potential way to infer this information, yet whether generated videos preserve complementary diagnostic evidence across views remains unclear. We introduce ECGEchoDiag, a dataset and benchmark for ECG-conditioned multi-view Echo video generation and evaluation of diagnostic information preservation. It contains 15,715 synchronized ECG–Echo cardiac cycle pairs from 991 patients across 10 views, including 507 patients with multi-label annotations for eight cardiac disease categories. The dataset preserves within-patient associations across views and temporal synchronization within each recording. We further propose EchoGenDiag, which combines target-aware cross-view conditioning, cardiac-phase-dependent motion modeling, and reference-guided video reconstruction. For each target view, the model integrates its reference frame and ECG with information from other available views of the same examination. A frozen diagnostic teacher provides multi-label supervision and prediction consistency constraints to encourage disease-related information preservation. We evaluate reconstruction quality, temporal consistency, and patient-level multi-label disease recognition using patient-disjoint splits. Comparisons between real and generated videos assess diagnostic information preservation, while single-view and multi-view comparisons examine the contribution of cross-view conditioning.