TY  - GEN
AB  - Acute compartment syndrome (ACS) is an orthopedic emergency caused by elevated pressure within a muscle compartment that can lead to permanent tissue damage and death. Current ACS diagnosis relies heavily on patient-reported symptoms, a method that is subjective and often followed by invasive intracompartmental pressure measurements that can be faulty in motion settings. Reliable motion diagnosis is critical for long-term monitoring that involves limb movement. This study proposes an objective and noninvasive diagnostic for ACS. Our device utilizes a random forest machine learning model that uses analog readings from force-sensitive resistors (FSRs) placed on the skin. To validate the machine learning diagnostic model, a data set containing FSR measurements and the corresponding simulated pressure was created for motion and motionless scenarios. Our diagnostic achieved up to 98% accuracy and excelled in key performance metrics, including sensitivity (97%) and specificity (98%), with a statistically insignificant (±5% error bars) performance difference in motion present cases. These results demonstrate the potential of noninvasive ACS diagnostics to meet clinical accuracy standards in real world scenarios.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AU  - Abu Hweij, Zaina
AU  - Liang, Florence
AU  - Zhang, Sophie
DA  - 2024
DO  - 10.6083/bpxhc43676
DO  - doi
ID  - 43676
KW  - Orthopedics
KW  - Compartment Syndromes
KW  - Random Forest
KW  - Machine Learning
KW  - Muscles
KW  - Musculoskeletal System
KW  - flexible pressure sensors
KW  - noninvasive diagnostic
L1  - https://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
L2  - https://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
L4  - https://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
N2  - Acute compartment syndrome (ACS) is an orthopedic emergency caused by elevated pressure within a muscle compartment that can lead to permanent tissue damage and death. Current ACS diagnosis relies heavily on patient-reported symptoms, a method that is subjective and often followed by invasive intracompartmental pressure measurements that can be faulty in motion settings. Reliable motion diagnosis is critical for long-term monitoring that involves limb movement. This study proposes an objective and noninvasive diagnostic for ACS. Our device utilizes a random forest machine learning model that uses analog readings from force-sensitive resistors (FSRs) placed on the skin. To validate the machine learning diagnostic model, a data set containing FSR measurements and the corresponding simulated pressure was created for motion and motionless scenarios. Our diagnostic achieved up to 98% accuracy and excelled in key performance metrics, including sensitivity (97%) and specificity (98%), with a statistically insignificant (±5% error bars) performance difference in motion present cases. These results demonstrate the potential of noninvasive ACS diagnostics to meet clinical accuracy standards in real world scenarios.
PB  - Oregon Health and Science University
PY  - 2024
T1  - Noninvasive acute compartment syndrome diagnosis using random forest machine learning
TI  - Noninvasive acute compartment syndrome diagnosis using random forest machine learning
UR  - https://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
Y1  - 2024
ER  -