Objectives: To assess the lung involvement in patients with Still's disease, an inflammatory disease assessing both children and adults. To exploit possible associated factors for parenchymal lung involvement in these patients.
Methods: A multicentre observational study was arranged assessing consecutive patients with Still's disease characterized by the lung involvement among those included in the AIDA (AutoInflammatory Disease Alliance) Network Still's Disease Registry.
Objective: We aimed to (1) evaluate the cardiac involvement, with a focus on myocarditis, in patients with Still disease included in the multicenter Autoinflammatory Disease Alliance (AIDA) Network Still disease registry; and (2) assess the predictive factors for myocarditis by deriving a clinical risk patient profile for this severe manifestation.
Methods: A multicenter observational study was established, in which consecutive patients with Still disease in the AIDA Network Still disease registry were characterized by cardiac involvement. Cardiac involvement was defined according to the presence of pericarditis, tamponade, myocarditis, and/or aseptic endocarditis.
This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas. AI, including machine learning (ML) and deep learning (DL), is revolutionizing diagnosis, treatment personalization, and prognosis prediction in rheumatologic diseases. Specifically, AI models based on convolutional neural networks (CNNs) demonstrate significant efficacy in analyzing medical images for disease classification and severity assessment.
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