Rare cardiovascular diseases, such as congenital heart disease, are difficult to study given that very few patients are available; our pathophysiological knowledge often is limited; and treatment or prevention is often lacking. This makes that common approaches in medicine, where clinical studies are performed or data science can mine large amounts of data, cannot be used. Therefore, the approach in our lab is to combine whatever sources of information available and use pathophysiological knowledge, enriched with interpretable machine learning, that can be used in small datasets, to gain a better understanding of the disease process, suggest new diagnostic tools and potentially help in improving treatment. In this lecture, besides the overall approach used in the lab, our integrated view will be illustrated with fetuses with suspected Coarctation of the Aorta, a congenital heart disease that is often difficult to diagnose prenatally. First, we have performed a clinical study to assess the hearts and large vessels of the fetuses, providing a hypothesis for the expected changes in cardiovascular structure and function. Next, we created (simplified) computational models to test some of the proposed mechanisms. Finally, we use manifold learning on complex clinical and imaging data to compare individual patients and provide potential diagnostic tools.<\/p>\n
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