The role and challenge of knee cartilage MRI in early diagnosis of knee osteoarthritis: a literature review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, the Second Affiliated Hospital of Naval Medical, Navy Medical University, PLA, Shanghai, China.
- Department of Radiology, the Second Affiliated Hospital of Naval Medical, Navy Medical University, PLA, Shanghai, China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- Department of Radiology, the Second Affiliated Hospital of Naval Medical, Navy Medical University, PLA, Shanghai, China. Electronic address: [email protected].
Abstract
Osteoarthritis (OA) is one of the most prevalent diseases in society today, resulting in significant socio-economic costs, and knee osteoarthritis (KOA) is the most common type. Articular cartilage degeneration is the key pathological change of OA, and cartilage plays a vital role in early diagnosis. With the advancement of science and technology, MRI became widely used in KOA as a non-invasive imaging tool for direct display of articular cartilage. This article explores the advantages, limitations, and improvement strategies of knee cartilage MRI in early diagnosis of KOA, aiming to provide a comprehensive understanding of its clinical significance. Compositional MRI can detect changes in tissue biochemical components before morphological changes occur, and quantitative and semi-quantitative methods can systematically evaluate minor changes in cartilage, exploring new biomarkers for early detection. Under the guidance of deep learning, high-resolution 3D technology, and super-resolution reconstruction technology, cartilage can be assessed more accurately and quickly, accelerating clinical transformation and enabling early intervention and management in the early stage of reversible cartilage recovery. Knee cartilage MRI can help us better perform early diagnosis of KOA. We should grasp the cartilage MRI in the early stage of the disease, improve the early diagnosis and treatment process and diagnostic classification criteria, accelerate the development of deep learning algorithms and new scanning methods, and push new technologies to the clinic as soon as possible.