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Artificial intelligence in rheumatology research: what is it good for?
RMD Open
January 2025
Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support.
View Article and Find Full Text PDFAdvancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review.
Eur Heart J Imaging Methods Pract
October 2024
Cardiologia 1-Emodinamica, Dipartimento Cardiotoracovascolare 'A. De Gasperis', ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy.
Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists.
View Article and Find Full Text PDFMicro- and milli-fluidic sample environments for X-ray analysis in the chemical and materials sciences.
Lab Chip
January 2025
Université Paris-Saclay, CEA, CNRS, NIMBE, LIONS, 91191, Gif-sur-Yvette, France.
X-ray-based methods are powerful tools for structural and chemical studies of materials and processes, particularly for performing time-resolved measurements. In this critical review, we highlight progress in the development of X-ray compatible microfluidic and millifluidic platforms that enable high temporal and spatial resolution X-ray analysis across the chemical and materials sciences. With a focus on liquid samples and suspensions, we first present the origins of microfluidic sample environments for X-ray analysis by discussing some alternative liquid sample holder and manipulator technologies.
View Article and Find Full Text PDFIdentifying Biomarkers for Atherosclerosis via Gene Expression and Biological Networking.
Curr Cardiol Rev
January 2025
School of Paramedics and Allied Health Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India.
Background: Atherosclerosis is a chronic disease caused by the accumulation of lipids, inflammatory cells, and fibrous elements in arterial walls, leading to plaque formation and cardiovascular conditions like coronary artery disease, stroke, and peripheral arterial disease. Factors like hyperlipidemia, hypertension, smoking, and diabetes contribute to its development. Diagnosis relies on imaging and biomarkers, while management includes lifestyle modifications, pharmacotherapy, and surgical interventions.
View Article and Find Full Text PDFImage Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.
Sensors (Basel)
December 2024
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Jeollanam-do, Republic of Korea.
Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes.
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