Pulmonary Embolism

Pulmonary embolism (PE), a life-threatening blockage in the lungs' arteries, is a significant cause of mortality, demanding accurate and timely diagnosis. Current research heavily focuses on developing computer-aided detection (CAD) systems using deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures, to analyze computed tomography pulmonary angiography (CTPA) images and integrate clinical data (e.g., electronic health records) for improved PE detection and mortality risk prediction. These multimodal approaches aim to enhance diagnostic accuracy, reduce diagnostic delays, and ultimately improve patient outcomes by providing clinicians with more reliable and efficient tools.