Pulmonary Embolism Diagnosis

Pulmonary embolism (PE) diagnosis aims to rapidly and accurately identify this life-threatening condition, often hampered by atypical symptoms. Current research heavily emphasizes the development of computer-aided diagnosis (CAD) systems using deep learning, particularly convolutional neural networks (CNNs), often incorporating attention mechanisms and multi-task learning frameworks, to analyze computed tomography pulmonary angiography (CTPA) and even non-contrast CT scans. These AI-driven approaches aim to improve diagnostic speed and accuracy, potentially reducing mortality by assisting radiologists in interpreting large volumes of complex imaging data and integrating information from electronic health records. The ultimate goal is to improve patient care through faster and more reliable PE detection.