Chest Computed Tomography

Chest computed tomography (CT) analysis is crucial for diagnosing and monitoring various lung diseases, with current research heavily focused on leveraging deep learning to automate and improve diagnostic accuracy. Studies employ convolutional neural networks (CNNs), Vision Transformers (ViTs), and U-Net architectures, often combined with techniques like multi-task learning and anomaly detection, to perform tasks such as lesion segmentation, disease classification (including COVID-19 severity), and even prediction of pulmonary function from CT scans. These advancements offer the potential for faster, more efficient, and potentially more accurate diagnoses, improving patient care and reducing the burden on healthcare systems.