McDonald Steel M, Felfeliyan Banafshe, Hassan Ali, Küpper Jessica C, El-Hajj Rehab, Wichuk Stephanie, Aneja Ashmeen, Kwok Cherise, Zhang Cindy X Y, Jans Lennart, Herregods Nele, Hareendranathan Abhilash R, Jaremko Jacob L
Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada.
Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.
Skeletal Radiol. 2025 Mar 26. doi: 10.1007/s00256-025-04922-5.
This systematic review explores key quantitative and semi-quantitative MRI-based scoring systems for arthritis biomarkers, focusing on their potential for automation through AI.
A systematic review of Medline, PubMed, and Scopus from 2014 to 2024. Keywords included MRI, arthritis, and quantitative/semi-quantitative. From the initial retrieval of 3321 papers, after exclusions, we evaluated the full-text for 129 studies from the past decade, 74 of which related specifically to knee osteoarthritis.
Publications on MRI arthritis scoring systems peaked in 2021 and have declined in recent years, likely due to a shift toward AI-driven automation. We assessed scoring systems for biomarkers including cartilage thickness, bone marrow edema, effusion/synovitis, erosions, osteophytes, intraosseous and periarticular fat metaplasia, and connective tissue integrity (meniscus/labrum), each varying in suitability for AI. Effusion, due to its high MRI T2 contrast, appears relatively straightforward to automate, while cartilage loss remains difficult to accurately quantify and localize despite heavy research interest. AI demonstrates suitability in meniscal tear detection and the potential to automate other biomarkers like BMEs, bone erosion, and osteophyte formation.
AI is increasingly being used to automatically evaluate MRI for arthritis. This review identifies opportunities for AI to enhance longitudinal disease tracking and enable early intervention in arthritis by providing detailed scoring of inflammatory lesions and high-resolution evaluation of structural abnormalities.
本系统评价探讨了基于磁共振成像（MRI）的关键定量和半定量关节炎生物标志物评分系统，重点关注其通过人工智能实现自动化的潜力。
对2014年至2024年的医学文献数据库（Medline）、PubMed和Scopus进行系统评价。关键词包括MRI、关节炎和定量/半定量。在最初检索到的3321篇论文中，经过筛选，我们对过去十年的129项研究进行了全文评估，其中74项专门涉及膝关节骨关节炎。
关于MRI关节炎评分系统的出版物在2021年达到峰值，近年来有所下降，可能是由于向人工智能驱动的自动化转变。我们评估了生物标志物的评分系统，包括软骨厚度、骨髓水肿、积液/滑膜炎、侵蚀、骨赘、骨内和关节周围脂肪化生以及结缔组织完整性（半月板/盂唇），每种在人工智能适用性方面各不相同。由于其在MRI T2上的高对比度，积液似乎相对容易实现自动化，而尽管有大量研究兴趣，但软骨损失仍难以准确量化和定位。人工智能在半月板撕裂检测中显示出适用性，并且有潜力实现其他生物标志物的自动化，如骨髓水肿、骨侵蚀和骨赘形成。
人工智能越来越多地用于自动评估关节炎的MRI。本综述确定了人工智能的机会，通过对炎症病变进行详细评分和对结构异常进行高分辨率评估，来加强对疾病的纵向跟踪并实现关节炎的早期干预。
