TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction
Diane Kima, Minh Nguyen Nhat Toa, Sherif Abdallab c, Teresa S.M. Tsangb c, Purang Abolmaesumia, Christina L. Luongb c✉
- aUniversity of British Columbia, Vancouver, BC, Canada
- bVancouver Coastal Health, Vancouver, BC, Canada
- cVancouver General Hospital, Vancouver, BC, Canada
- ✉Corresponding author
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Abstract
Coronary angiography remains the gold standard for diagnosing Acute Coronary Syndrome (ACS). However, its resource-intensive and invasive nature can expose patients to procedural risks and diagnostic delays, leading to postponed treatment initiation. In this work, we introduce TREAT-Net, a multimodal deep learning framework for ACS treatment prediction that leverages non-invasive modalities, including echocardiography videos and structured clinical records. TREAT-Net integrates tabular-guided cross-attention to enhance video interpretation, along with a late fusion mechanism to align predictions across modalities. Trained on a dataset of over 9000 ACS cases, the model outperforms unimodal and non-fused baselines, achieving a balanced accuracy of 67.6% and an AUROC of 71.1%. Cross-modality agreement analysis demonstrates 88.6% accuracy for intervention prediction. These findings highlight the potential of TREAT-Net as a non-invasive tool for timely and accurate patient triage, particularly in underserved populations with limited access to coronary angiography.
TL;DRTREAT-Net is introduced, a multimodal deep learning framework for ACS treatment prediction that leverages non-invasive modalities, including echocardiography videos and structured clinical records, and outperforms unimodal and non-fused baselines. (Semantic Scholar)