Pulmonary Hypertension

Pulmonary hypertension (PH), characterized by elevated blood pressure in the pulmonary arteries, is a serious condition requiring accurate and timely diagnosis. Current research focuses on developing non-invasive diagnostic methods, primarily leveraging machine learning models (e.g., convolutional neural networks, gradient boosting trees, and recurrent neural networks) trained on data from cardiac magnetic resonance imaging (MRI), electrocardiograms (ECGs), and electronic health records (EHRs) to estimate key hemodynamic parameters like mean pulmonary artery pressure (mPAP). These advancements aim to replace the invasive gold-standard procedure (right heart catheterization), improving patient care and potentially enabling earlier, more effective interventions. The development of robust and interpretable AI-driven diagnostic tools holds significant promise for improving PH diagnosis and management.