Wu Wei-Ting, Shu Yi-Chung, Lin Che-Yu, Gonzalez-Suarez Consuelo B, Özçakar Levent, Chang Ke-Vin
Department of Physical Medicine and Rehabilitation and Community and Geriatric, National Taiwan University Hospital, Bei-Hu Branch, Taipei, Taiwan.
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, Taipei, Taiwan.
J Imaging Inform Med. 2025 Sep 12. doi: 10.1007/s10278-025-01661-w.
Shoulder pain is a common musculoskeletal complaint requiring accurate imaging for diagnosis and management. Ultrasound is favored for its accessibility, dynamic imaging, and high-resolution soft tissue visualization. However, its operator dependency and variability in interpretation present challenges. Recent advancements in artificial intelligence (AI), particularly deep learning algorithms like convolutional neural networks, offer promising applications in musculoskeletal imaging, enhancing diagnostic accuracy and efficiency. This narrative review explores AI integration in shoulder ultrasound, emphasizing automated pathology detection, image segmentation, and outcome prediction. Deep learning models have demonstrated high accuracy in grading bicipital peritendinous effusion and discriminating rotator cuff tendon tears, while machine learning techniques have shown efficacy in predicting the success of ultrasound-guided percutaneous irrigation for rotator cuff calcification. AI-powered segmentation models have improved anatomical delineation; however, despite these advancements, challenges remain, including the need for large, well-annotated datasets, model generalizability across diverse populations, and clinical validation. Future research should optimize AI algorithms for real-time applications, integrate multimodal imaging, and enhance clinician-AI collaboration.
肩部疼痛是一种常见的肌肉骨骼疾病，需要通过精确的影像学检查来进行诊断和治疗。超声检查因其易于操作、动态成像以及高分辨率的软组织可视化效果而受到青睐。然而，其对操作者的依赖性以及解读结果的变异性带来了挑战。人工智能（AI）领域的最新进展，特别是卷积神经网络等深度学习算法，在肌肉骨骼成像中展现出了有前景的应用，提高了诊断的准确性和效率。这篇叙述性综述探讨了人工智能在肩部超声中的应用，重点介绍了自动病理检测、图像分割和结果预测。深度学习模型在肱二头肌周围腱膜积液分级和区分肩袖肌腱撕裂方面已显示出高准确性，而机器学习技术在预测超声引导下经皮冲洗治疗肩袖钙化的成功率方面也已证明有效。人工智能驱动的分割模型改善了解剖结构的描绘；然而，尽管有这些进展，挑战依然存在，包括需要大量标注良好的数据集、模型在不同人群中的通用性以及临床验证。未来的研究应优化人工智能算法以用于实时应用，整合多模态成像，并加强临床医生与人工智能的协作。
