【摘要】 肌骨超声（MSUS）作为诊断和治疗周围神经疾病的重要工具，其临床应用仍面临操作者依赖性高、微小病变识别困难及定量分析缺乏标准化等局限性。人工智能（AI）技术，尤其是深度学习在医学影像领域的快速发展，为突破这些瓶颈提供了创新解决方案。笔者系统综述AI技术在MSUS诊疗周围神经疾病中的应用现状与发展前景，重点探讨其在神经结构自动分割与测量、病变检测与分类、超声引导下介入优化等核心环节的应用价值。同时，文章进一步分析了当前AI临床应用面临的局限性，包括数据标准化、模型泛化能力及伦理法规等问题，并提出了多学科专家达成的实施建议，以推动AI技术在该领域的规范化、标准化与临床转化，最终助力实现更精准、高效的周围神经疾病诊疗模式。更多还原

【Abstract】 Musculoskeletal Ultrasound(MSUS) is an important tool for the diagnosis and treatment of peripheral nerve diseases, but its clinical application still faces limitations such as high operator dependence, difficulty in identifying subtle lesions, and lack of standardization in quantitative analysis. The rapid development of artificial intelligence(AI) technology, especially deep learning in the field of medical imaging, has provided innovative solutions to break through these bottlenecks. The authors systematically review the current application status and development prospects of AI technology in MSUS for the diagnosis and treatment of peripheral nerve diseases, focusing on its application value in core links including automatic segmentation and measurement of nerve structures, lesion detection and classification, and optimization of ultrasound-guided interventions. Meanwhile, the authors of this article further analyze the current limitations of AI in clinical application, such as data standardization, model generalization ability, and ethical regulations, and puts forward implementation suggestions reached by multidisciplinary experts. These aims to promote the regularization, standardization and clinical translation of AI technology in this field, and ultimately help achieve a more accurate and efficient diagnosis and treatment model for peripheral nerve diseases.更多还原