标题： 人工智能驱动的骨质疏松症医学影像诊断方法综述 显示英文标题

标题： Survey of AI-Powered Approaches for Osteoporosis Diagnosis in Medical Imaging

摘要： 骨质疏松症在全球范围内悄然侵蚀骨骼完整性；然而，通过影像学检查早期发现可以预防大多数脆性骨折。 人工智能（AI）方法现在从常规双能X射线吸收测定法（DXA）、X光、计算机断层扫描（CT）和磁共振成像（MRI）扫描中挖掘细微的、具有临床行动价值的标志物，但文献仍零散。 本综述通过一个三轴框架统一该领域，该框架将影像学模式与临床任务和AI方法（经典机器学习、卷积神经网络（CNNs）、变换器、自监督学习和可解释AI）相结合。 在简明的临床和技术简介之后，我们详细介绍了基于PRISMA指南的搜索策略，通过路线图图示介绍分类法，并综合各研究之间的见解，包括数据稀缺性、外部验证和可解释性。 通过识别新兴趋势、开放挑战和可行的研究方向，本综述为AI科学家、医学影像研究人员和骨骼肌肉科临床医生提供了一个清晰的指南，以加速骨质疏松症护理中的严谨、以患者为中心的创新。 本综述的项目页面也可以在Github上找到。 显示英文摘要

摘要： Osteoporosis silently erodes skeletal integrity worldwide; however, early detection through imaging can prevent most fragility fractures. Artificial intelligence (AI) methods now mine routine Dual-energy X-ray Absorptiometry (DXA), X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) scans for subtle, clinically actionable markers, but the literature is fragmented. This survey unifies the field through a tri-axial framework that couples imaging modalities with clinical tasks and AI methodologies (classical machine learning, convolutional neural networks (CNNs), transformers, self-supervised learning, and explainable AI). Following a concise clinical and technical primer, we detail our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search strategy, introduce the taxonomy via a roadmap figure, and synthesize cross-study insights on data scarcity, external validation, and interpretability. By identifying emerging trends, open challenges, and actionable research directions, this review provides AI scientists, medical imaging researchers, and musculoskeletal clinicians with a clear compass to accelerate rigorous, patient-centered innovation in osteoporosis care. The project page of this survey can also be found on Github.