Harnessing Artificial Intelligence for Shoulder Ultrasonography: A Narrative Review.
Authors
Affiliations (9)
Affiliations (9)
- 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.
- Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei, Taiwan.
- Research Center for Health Science, University of Santo Tomas, Manila, Philippines.
- Department of Physical Medicine and Rehabilitation, Our Lady of Lourdes Hospital, Manila, Philippines.
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey.
- Department of Physical Medicine and Rehabilitation and Community and Geriatric, National Taiwan University Hospital, Bei-Hu Branch, Taipei, Taiwan. [email protected].
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, Taipei, Taiwan. [email protected].
- Center for Regional Anesthesia and Pain Medicine, Wang-Fang Hospital, Taipei Medical University, Taipei, Taiwan. [email protected].
Abstract
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.