TREAT-Netv2: regional wall motion-informed video-tabular fusion for ACS treatment prediction
Diane Kim1, Victoria Wu2, Minh Nguyen Nhat To3
1University of British Columbia, Vancouver, BC, Canada. daeun96@student.ubc.ca.
Purpose:
Acute coronary syndrome (ACS) is a major cause of cardiovascular mortality. While coronary angiography enables definitive diagnosis and intervention, its invasiveness and limited availability delay treatment, disproportionately affecting rural and remote communities. Development of noninvasive, predictive tools for early revascularization may improve triage and outcomes.
Methods:
We propose TREAT-Netv2, a regional wall motion-informed video-tabular fusion network for ACS treatment prediction that integrates echocardiograms (echo) and electronic medical records. The model extracts regional wall motion features from echo sequences and applies the transformer-based multiple instance learning to capture nuanced disease representations. TREAT-Netv2 does not require diagnostic details such as level of occlusion or ACS subtype, eliminating the need for additional procedures and improving its robustness.
Results:
TREAT-Netv2 achieved an AUROC of 72.5% and balanced accuracy of 68.6%, outperforming unimodal, multimodal, and state-of-the-art baselines. ACS subgroup analysis showed that TREAT-Netv2 achieved the highest accuracy for non-ST-elevated myocardial infarction and unstable angina (NSTEMI/UA) patients, the most clinically challenging cases where the need for invasive intervention is often uncertain.
Conclusion:
By the complete elimination of ACS-specific diagnostic inputs and incorporation of transformer-based fusion, TREAT-Netv2 enables noninvasive and resource-free ACS risk stratification, particularly in clinically ambiguous cases. Our code will be made publicly available at URL: github.com/DeepRCL/TREAT-Netv2.
