Artificial Intelligence and Deep Learning in Endobronchial Ultrasound

This chapter explores the emerging role of Artificial Intelligence (AI) and Deep Learning (DL) in enhancing the capabilities of Endobronchial Ultrasound (EBUS). It examines how AI and DL technologies are being integrated into EBUS systems to improve image analysis, interpretation, and diagnostic accuracy. The chapter discusses the development of AI models that assist in identifying and characterizing lesions, evaluating lymph nodes, and predicting patient outcomes based on ultrasound images. It also highlights the AI-driven tools to assist clinicians in decision-making, reduce inter-observer variability, and enhance procedural efficiency. This chapter also offers valuable insights into how AI and deep learning models are revolutionizing EBUS, paving the way for more precise and accurate diagnosis. Artificial intelligence (AI) is being widely used for interpretation of medical images like chest X-rays, CT scan and MRI and it has been found to improve the diagnosis and classification of diseases. The different types of AI models have been found to have exceeded the expectations of human sciences. AI has few components like machine learning and deep learning which can be used to create models for identifying a disease condition.