000043676 001__ 43676
000043676 005__ 20240822075701.0
000043676 0247_ $$2doi$$a10.6083/bpxhc43676
000043676 037__ $$aIR
000043676 041__ $$aeng
000043676 245__ $$aNoninvasive acute compartment syndrome diagnosis using random forest machine learning
000043676 260__ $$bOregon Health and Science University
000043676 269__ $$a2024
000043676 336__ $$aAbstract
000043676 520__ $$aAcute compartment syndrome (ACS) is an orthopedic emergency caused by elevated pressure within a muscle compartment that can lead to permanent tissue damage and death. Current ACS diagnosis relies heavily on patient-reported symptoms, a method that is subjective and often followed by invasive intracompartmental pressure measurements that can be faulty in motion settings. Reliable motion diagnosis is critical for long-term monitoring that involves limb movement. This study proposes an objective and noninvasive diagnostic for ACS. Our device utilizes a random forest machine learning model that uses analog readings from force-sensitive resistors (FSRs) placed on the skin. To validate the machine learning diagnostic model, a data set containing FSR measurements and the corresponding simulated pressure was created for motion and motionless scenarios. Our diagnostic achieved up to 98% accuracy and excelled in key performance metrics, including sensitivity (97%) and specificity (98%), with a statistically insignificant (±5% error bars) performance difference in motion present cases. These results demonstrate the potential of noninvasive ACS diagnostics to meet clinical accuracy standards in real world scenarios.
000043676 540__ $$fCC BY
000043676 542__ $$fIn copyright - joint owners
000043676 650__ $$aOrthopedics$$023320
000043676 650__ $$aCompartment Syndromes$$016953
000043676 650__ $$aRandom Forest$$013949
000043676 650__ $$aMachine Learning$$011449
000043676 650__ $$aMuscles$$022535
000043676 650__ $$aMusculoskeletal System$$022544
000043676 6531_ $$aflexible pressure sensors
000043676 6531_ $$anoninvasive diagnostic
000043676 7001_ $$aAbu Hweij, Zaina$$uOregon Health and Science University$$041354
000043676 7001_ $$aLiang, Florence$$uOregon Health and Science University$$041354
000043676 7001_ $$aZhang, Sophie$$uOregon Health and Science University$$041354
000043676 711__ $$aResearch Week$$uOregon Health and Science University$$d2024
000043676 8564_ $$9dc9e0fdd-818e-4e3c-90ad-11fd7f9cf46e$$s220995$$uhttps://digitalcollections.ohsu.edu/record/43676/files/ResearchWeek.2024.AbuHweij.Zaina.pdf
000043676 980__ $$aResearch Week