Lung Ultrasound

Lung ultrasound (LUS) analysis is rapidly advancing, driven by the need for rapid, point-of-care diagnostics and remote monitoring, particularly highlighted by the COVID-19 pandemic. Current research focuses on developing and improving AI-powered diagnostic tools using deep learning architectures like convolutional neural networks (CNNs) and YOLO, often incorporating techniques like self-supervised pretraining, few-shot learning, and data augmentation with synthetic images to overcome data scarcity. These advancements aim to improve the accuracy, efficiency, and interpretability of LUS image analysis for tasks such as disease detection (e.g., pneumothorax, COVID-19 pneumonia), severity scoring, and landmark identification, ultimately enhancing clinical decision-making and patient care.