Artificial Intelligence and Machine Learning in Rotator Cuff Tears.

Rodriguez, Hugo C; Rust, Brandon; Hansen, Payton Yerke; Maffulli, Nicola; Gupta, Manu; Potty, Anish G; Gupta, Ashim

Abstract

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management. [Abstract copyright: Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.]

Citation

Rodriguez, H. C., Rust, B., Hansen, P. Y., Maffulli, N., Gupta, M., Potty, A. G., & Gupta, A. (in press). Artificial Intelligence and Machine Learning in Rotator Cuff Tears. Sports Medicine and Arthroscopy Review, 31(3), 67-72. https://doi.org/10.1097/JSA.0000000000000371

• Journal Article Type: Article
• Acceptance Date: Nov 17, 2023
• Online Publication Date: Nov 17, 2023
• Deposit Date: Dec 4, 2023
• Journal: Sports medicine and arthroscopy review
• Print ISSN: 1062-8592
• Electronic ISSN: 1538-1951
• Publisher: Lippincott, Williams & Wilkins
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 3
• Pages: 67-72
• DOI: https://doi.org/10.1097/JSA.0000000000000371
• Keywords: Magnetic Resonance Imaging, Rotator Cuff - diagnostic imaging - surgery, Humans, Rotator Cuff Injuries - diagnostic imaging - surgery, Machine Learning, Artificial Intelligence