Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases
Antani, Sameer (editor)
Rajaraman, Sivaramakrishnan (editor)
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.
Keywordslung; 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; cardiac amyloidosis; AL/TTR amyloidosis; hypertrophic cardiomyopathy; left ventricular hypertrophy; convolutional neural network; Tuberculosis (TB); drug resistance; chest X-rays; generalization; localization; Electrical Impedance Tomography; lung imaging; cardiopulmonary monitoring; aorta; lung cancer; pulmonary artery; pulmonary hypertension; modality-specific knowledge; object detection; RetinaNet; ensemble learning; pneumonia; mean average precision; source data set; supervised classification; coronary artery disease; machine learning; cardiopulmonary disease; faster CNN; medical imaging; X-rays; transfer learning; explainability; n/a
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Publication date and placeBasel, 2023
Technology: general issues
History of engineering & technology