Graphical overview illustrating the application of AI-based techniques in analyzing MRI data for improved diagnosis and predictive modeling in the field of rheumatology . This review refers to applications for rheumatoid arthritis , spondyloarthritis , myopathy and systemic sclerosis . Parts of this figure were created with BioRender.com. Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
No takes yet. Share an insight, caveat, or question.
Adams et al. (2023) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: