综述AI在骨质疏松影像诊断中的应用，整合多模态技术与临床任务
Survey of AI-Powered Approaches for Osteoporosis Diagnosis in Medical Imaging
- 构建影像模态、临床任务与AI方法的三轴分类框架
- 指出数据稀缺、外部验证不足与可解释性差等共性挑战
- 适合医学影像研究者与AI科学家参考，指引未来研究方向
骨质疏松在全球范围内悄然破坏骨骼结构，但通过影像学早期检测可预防多数脆性骨折。当前人工智能（AI）正从常规双能X线吸收测定法（DXA）、X光、计算机断层扫描（CT）和磁共振成像（MRI）中挖掘细微且具临床意义的标志物，但文献分散。本综述提出一个三轴框架，将成像模态、临床任务与AI方法（传统机器学习、卷积神经网络（CNN）、Transformer、自监督学习、可解释AI）相耦合。在简明的临床与技术导论后，采用遵循系统综述与元分析首选报告项目（PRISMA）的检索策略，通过路线图展示分类体系，并综合跨研究洞察，涵盖数据稀缺性、外部验证及可解释性问题。通过识别新兴趋势、开放挑战与可操作的研究方向，本综述为AI科学家、医学影像研究人员及骨科临床医生提供清晰指引，以加速严谨且以患者为中心的骨质疏松诊疗创新。该项目页面亦可在Github上获取。
原文摘要 · Abstract (English)
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.
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