Latest AI and machine learning research in copd for healthcare professionals.
PURPOSE: To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.
Artificial intelligence (AI) is redefining medical education, bringing new dimensions of personalized learning, enhanced visualization and simulation-based clinical training to the forefront. Additionally, AI-powered simulations offer realistic, immersive training opportunities, preparing students for complex clinical situations and fostering interprofessional collaboration skills essential for mo...
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and ...
Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent exa...
Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - ...
Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study pr...
Mental health remains a challenging problem all over the world, with issues like depression, anxiety becoming increasingly common. Large Language Mo...
Until now, in the wake of the COVID-19 pandemic in 2019, lung diseases, especially diseases such as lung cancer and chronic obstructive pulmonary di...
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs...
Language is far more than a communication tool. A wealth of information - including but not limited to the identities, psychological states, and soc...
Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual ...
As CT imaging is increasingly used for the evaluation of lung nodules and the diagnosis and screening of lung cancer in smokers, we have more opportun...
Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney bi...
Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great ...
Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue ...
Visual object counting has recently shifted towards class-agnostic counting (CAC), which addresses the challenge of counting objects across arbitrar...
Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multi...
This study introduces RelCAT (Relation Concept Annotation Toolkit), an interactive tool, library, and workflow designed to classify relations betwee...
The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in ...
Ensuring safety alignment has become a critical requirement for large language models (LLMs), particularly given their widespread deployment in real...