Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, USA.
Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
Nat Rev Rheumatol. 2022 Feb;18(2):112-121. doi: 10.1038/s41584-021-00719-7. Epub 2021 Nov 30.
The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.
MRI 的 3D 性质和软组织对比使其成为骨关节炎研究的宝贵工具，有助于阐明疾病的发病机制和进展。最近 MRI 的应用越来越广泛，这当然是由于在研究方面的大量投资所带来的重大进展所推动的，特别是与人工智能（AI）相关的研究。这些与 AI 相关的进展正在通过增强从图像采集到后处理等各种活动，彻底改变 MRI 在临床研究中的应用。自动化是减少 MRI 采集时间长、进行大规模纵向研究以及定量定义各种解剖关节中软、硬组织形态和其他重要临床特征的关键。深度学习方法最近已被用于肌肉骨骼领域的多个应用，以加深对骨关节炎的理解。与劳动密集型的人工努力相比，基于人工智能的方法在成像的各个阶段以及后处理步骤中都具有优势和潜力，包括辅助诊断和预后。然而，基于人工智能的方法也存在局限性，包括人工智能模型的可解释性有限。鉴于人工智能社区高度致力于揭示与模型预测相关的不确定性，并提高其可解释性，我们设想未来的临床转化和人工智能算法的使用将不断增加，以支持临床医生优化患者护理。
