Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Edited by
February 2023
246 pages
  • ISBN978-3-0365-6434-0 (Hardback)
  • ISBN978-3-0365-6435-7 (PDF)

This book is a reprint of the Special Issue Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases that was published in

Medicine & Pharmacology
Public Health & Healthcare

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
lung; conventional radiography; diagnostic procedure; chronic obstructive pulmonary disease; COVID-19; computed tomography; lungs; variability; segmentation; hybrid deep learning; artificial intelligence; deep learning; computer-based devices; radiology; thoracic diagnostic imaging; chest X-ray; CT; observer tests; performance; lung CT images; nodule detection; VGG-SegNet; pre-trained VGG19; deep learning; cardiac amyloidosis; AL/TTR amyloidosis; hypertrophic cardiomyopathy; left ventricular hypertrophy; deep learning; convolutional neural network; Tuberculosis (TB); drug resistance; deep learning; chest X-rays; generalization; localization; artificial intelligence; deep learning; Electrical Impedance Tomography; lung imaging; cardiopulmonary monitoring; aorta; computed tomography; deep learning; lung cancer; pulmonary artery; pulmonary hypertension; chest X-ray; deep learning; modality-specific knowledge; object detection; RetinaNet; ensemble learning; pneumonia; mean average precision; convolutional neural network; deep learning; source data set; supervised classification; coronary artery disease; deep learning; machine learning; cardiopulmonary disease; faster CNN; medical imaging; X-rays; artificial intelligence; transfer learning; explainability; n/a