Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Skeletal Radiol. 2022 Feb;51(2):331-343. doi: 10.1007/s00256-021-03909-2. Epub 2021 Nov 4.
The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.
近年来，人工智能（AI）在骨关节炎（OA）应用方面的进展迅速，特别是深度学习在图像分类、病变检测、软骨分割和未来膝骨关节炎发展预测建模方面的创新。本文综述了 AI 在 OA 研究中的应用，首先描述了机器学习（ML）技术和工作流程，然后介绍了这些算法如何通过基于成像和非成像的 ML 模型用于 OA 分类任务。本文还将描述 AI 在 OA 研究中的深度学习应用，包括对 X 线自动检测 OA 严重程度、MR 图像检测软骨/半月板病变和软骨分割进行自动 T 定量分析。此外，还将介绍用于识别 OA 发展高风险个体的 ML 模型的信息。机器学习在 OA 和软骨成像中的未来愿景依赖于 AI 的实施，以优化成像方案、对软骨进行定量评估以及自动分析疾病负担，从而为放射科医生提供更快、更高效的工作流程，具有更高的可重复性和准确性。它还可能为个体患者提供风险评估工具，这是精准医疗的一个组成部分。
