Minimally Invasive, Bioadaptive Multimodal Sensor Probe with Safe Deployment for Real-Time Acute Compartment Syndrome Diagnosisopen access
- Authors
- Seo, Seung Gi; Kim, Seungyeob; Yoo, Seonggwang; Oh, Seyong; Luan, Haiwen; Lv, Zengyao; Kim, Bosung; Li, Shupeng; Lu, Di; Kim, Jong Uk; Park, Yaeshin; Lee, Jae Hee; Jo, Hyeon Bin; Westman, Amanda M.; Moritz, William; Ribaudo, Joseph; Huang, Yonggang; Pet, Mitchell A.; Jin, Sung Hun; Rogers, John A.
- Issue Date
- Jul-2025
- Publisher
- WILEY
- Keywords
- acute compartment syndrome diagnosis; multimodal sensor; minimally invasive; wireless operation
- Citation
- ADVANCED SCIENCE, v.12, no.33, pp 1 - 13
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- ADVANCED SCIENCE
- Volume
- 12
- Number
- 33
- Start Page
- 1
- End Page
- 13
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126231
- DOI
- 10.1002/advs.202506942
- ISSN
- 2198-3844
2198-3844
- Abstract
- Acute Compartment Syndrome (ACS) is a serious medical condition that arises from increased pressure within osteofascial compartments, leading to impaired blood flow and potential tissue damage. Early and accurate diagnosis is critical for preventing permanent damage. Current methods rely largely on qualitative assessments with limited accuracy, and those that exploit invasive pressure measurements often prove inadequate. Herein, a soft materials-based multimodal sensor probe is introduced, as well as the mechanical and thermal influences to monitor intra-compartmental pressure, tissue oxygen saturation (StO2), and blood flow simultaneously at a common location within an affected compartment. The system integrates three sensors into a thin, flexible probe capable of real-time, wireless data transmission. The device allows for continuous monitoring with high reproducibility and sensitivity, to enhance diagnostic accuracy relative to current clinical practice, with the potential to early diagnosis of an acute compartment syndrome that requires fasciotomies. Large animal model studies, including short- and intermediate-term reliability assessments, highlight the key engineering features. The results reveal expected inverse relationships between pressure, StO2, and flow rate under simulated compartment syndrome conditions. This multimodal approach enhances diagnostic precision, offers real-time insights, and promises to yield improved outcomes through a comprehensive, quantitative diagnosis of compartment syndrome.
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