Artificial intelligence in ultrasound imaging for carpal tunnel syndrome: what clinicians need to know.
Authors
Ambardar R,Abdulsalam AJ,Mishra A
Affiliations (3)
Affiliations (3)
- Department of Physical Medicine and Rehabilitation, Mubarak Al-Kabeer Hospital, Jabriya, Kuwait.
- Department of Physical Medicine and Rehabilitation, Mubarak Al-Kabeer Hospital, Jabriya, Kuwait. [email protected].
- Department of Computer Engineering, Penn State University, University Park, PA, USA.
Abstract
This review examines artificial intelligence (AI) applications in ultrasound imaging for carpal tunnel syndrome diagnosis. Deep learning models have achieved Dice coefficients exceeding 0.85 for median nerve segmentation and diagnostic accuracy with area under the curve values up to 0.926, often matching specialist performance. However, methodological limitations exist: only three of 13 included studies (23%) performed external validation, and most used single-center retrospective designs. No prospective clinical trials have demonstrated improved patient outcomes. We critically appraise study quality, discuss current limitations, and provide practical recommendations for clinicians considering AI integration into their practice.
Topics
Journal ArticleReview