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Artificial Intelligence in Fascial Ultrasound: A Structured Narrative Review and Evidence Map From Segmentation to
Guoning Yan1, Chenglei Fan1,2,3
1School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China.
Clinical Anatomy (New York, N.Y.)
|July 6, 2026
Summary
Artificial intelligence (AI) in fascial ultrasound shows technical feasibility but lacks clinical evidence. More research on benchmarks and validation is needed for AI-assisted fascial ultrasound in practice.
Area of Science:
- Ultrasound Imaging
- Artificial Intelligence
- Fascial Anatomy
Background:
- Artificial intelligence (AI) is expanding in medical imaging, including ultrasound.
- The specific application and evidence for AI in fascial ultrasound are not well-defined.
Purpose of the Study:
- To conduct a structured narrative review and evidence map of AI in fascial ultrasound.
- To assess the current state of research and identify gaps for clinical translation.
Main Methods:
- Synthesized 97 studies across four evidence categories: direct fascial-AI, foundational fascial-ultrasound, adjacent ultrasound-AI, and analogical medical-imaging AI.
- Focused on studies directly addressing fascial or myofascial ultrasound AI.
Main Results:
- Only 10 studies directly investigated AI for fascial or myofascial ultrasound.
- Six studies specifically focused on named fascial targets.
- Current evidence primarily supports the technical feasibility of AI in this domain.
Conclusions:
- AI-assisted fascial ultrasound is technically feasible but not ready for clinical deployment.
- Further research is required, including shared benchmarks, multicenter validation, and outcome-linked biomarker studies.
