Enhancing Pulmonary Care: The Pivotal Role of Segmentation and Automated Analysis in Advanced Pulmonary Imaging

Abstract

Abstract In the field of medical imaging, techniques like computed tomography (CT), magnetic resonance imaging (MRI), and X-rays are essential for diagnosing and monitoring diseases, particularly in pulmonary pathology. A significant development in this area is the application of automated segmentation and machine learning, enhancing the accuracy in diagnosing lung diseases, such as lung cancer. However, there remains a knowledge gap in fully understanding the potential and limitations of these technologies, especially across diverse clinical conditions and datasets. To address this gap, the paper delves into the integration of artificial intelligence with conventional imaging techniques, focusing primarily on the use of convolutional neural networks (CNNs) and transformer-based models in automated segmentation. This approach is pivotal in improving the detection rates and accuracy of diagnoses in complex pulmonary diseases. Findings indicate that AI-enhanced imaging significantly advances the early detection of pulmonary diseases, notably lung cancer, and reduces the time until diagnosis. Yet, challenges such as the necessity for diverse and comprehensive training data and the generalizability of algorithms, persist. Moreover, ethical considerations in the deployment of AI technologies in healthcare are crucial. In conclusion, while these technologies mark substantial progress in pulmonary imaging, it is essential to find the balance between technological advancements and ethical considerations. This balance is key to ensuring effective and equitable healthcare, maximizing the benefits of AI in medical imaging while maintaining patient trust and privacy.