Artificial Intelligence in Fascial Ultrasound: A Structured Narrative Review and Evidence Map From Segmentation to Translational Requirements.
Authors
Yan G,Fan C
Affiliations (3)
Affiliations (3)
- School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China.
- Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China.
- Shandong Key Laboratory of Neurorehabilitation, University of Health and Rehabilitation Sciences, Qingdao, China.
Abstract
Artificial intelligence is increasingly used in ultrasound imaging, but its role in fascial ultrasound remains unclear. This structured narrative review and evidence map synthesizes 97 studies across direct fascial-AI, foundational fascial-ultrasound, adjacent ultrasound-AI, and analogical medical-imaging AI evidence. Only 10 studies directly addressed fascial or myofascial ultrasound AI, and only six were strict studies of named fascial targets. Current evidence supports technical feasibility rather than clinical deployment. Shared benchmarks, external multicenter validation, and outcome-linked biomarker studies are needed before AI-assisted fascial ultrasound can be translated into routine practice.
Topics
Journal ArticleReview