@article{IR,
      recid = {43676},
      author = {Abu Hweij, Zaina and Liang, Florence and Zhang, Sophie},
      title = {Noninvasive acute compartment syndrome diagnosis using  random forest machine learning},
      publisher = {Oregon Health and Science University},
      address = {2024},
      number = {IR},
      abstract = {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.},
      url = {http://digitalcollections.ohsu.edu/record/43676},
      doi = {https://doi.org/10.6083/bpxhc43676},
}