A non-invasive prediction model for coronary artery stenosis severity based on multimodal data

This study developed a non-invasive, transformer-based multimodal prediction model for assessing coronary artery stenosis severity. By combining diverse data sources and advanced machine learning techniques, the model offers a clinically viable alternative to invasive diagnostic methods. The results highlight the potential of multimodal data integration in improving CAD diagnosis and patient care.