Coronary artery disease (CAD) is the most common cardiovascular disease and leads to high morbidity and mortality rates worldwide. Assessment of risk is recommended in every patient evaluated for suspected or newly diagnosed CAD, as it has a major impact on therapy decisions. Echocardiography and invasive coronary angiography (ICA) are indispensable in the risk stratification and treatment decision in these patients. Assessment of left ventricular function by echocardiography is recommended in all patients, as it is the strongest predictor of long-term survival. In patients with severe symptoms and a high likelihood of CAD, ICA is recommended. Even though the images may contain valuable information, current risk stratification tools are limited to the low number of imaging and non-imaging parameters. Hence, the aim of this project is to investigate whether AI-based algorithms leveraging large datasets that include a rich set of imaging and clinical patient data improve risk stratification and clinical decision-making in patients with CAD. This project is a collaboration between the Department of Cardiology and it is funded by the Amsterdam UMC Impuls innovation grant.