Harnessing Artificial Intelligence for Shoulder Ultrasonography: A Narrative Review

Area of Science:

• Musculoskeletal imaging
• Artificial intelligence in medicine
• Diagnostic ultrasound

Background:

• Shoulder pain is a prevalent musculoskeletal issue necessitating precise imaging for diagnosis.
• Ultrasound is a preferred modality for shoulder assessment due to its accessibility and soft tissue visualization capabilities.
• Operator dependency and interpretation variability in ultrasound present diagnostic challenges.

Purpose of the Study:

• To review the integration and impact of artificial intelligence (AI) in shoulder ultrasound.
• To explore AI's role in automated pathology detection, image segmentation, and outcome prediction for shoulder conditions.
• To identify current challenges and future directions for AI in shoulder ultrasonography.

Main Methods:

• Narrative review of recent advancements in AI, specifically deep learning algorithms (e.g., convolutional neural networks), applied to shoulder ultrasound.
• Analysis of AI model performance in detecting and grading shoulder pathologies.
• Examination of AI for anatomical delineation and prediction of treatment outcomes.

Main Results:

• Deep learning models show high accuracy in grading bicipital peritendinous effusion and identifying rotator cuff tendon tears.
• Machine learning techniques effectively predict the success of ultrasound-guided percutaneous irrigation for rotator cuff calcification.
• AI-powered segmentation models have enhanced anatomical delineation in shoulder ultrasound images.

Conclusions:

• AI demonstrates significant potential to enhance diagnostic accuracy and efficiency in shoulder ultrasound.
• Challenges include the need for large datasets, model generalizability, and robust clinical validation.
• Future research should focus on real-time AI applications, multimodal imaging integration, and clinician-AI collaboration.