Acute compartment syndrome (ACS) is an orthopedic emergency, caused by
elevated pressure within a muscle compartment, that leads to permanent tissue
damage and eventually death. Diagnosis of ACS relies heavily on
patient-reported symptoms, a method that is clinically unreliable and often
supplemented with invasive intracompartmental pressure measurements. This study
proposes a continuous, objective, noninvasive diagnostic for ACS. The device
detects ACS through a random forest machine learning model that uses pressure
readings from force-sensitive resistors (FSRs) placed on the skin. The final
diagnosis is exported real-time to a web application via Bluetooth. To validate
the diagnostic, a data set containing FSR measurements and the corresponding
simulated intracompartmental pressure was created. The diagnostic achieved an
accuracy, on par to the invasive gold standard, of 97%. The device excelled in
key performance metrics including precision, sensitivity, and F1 score.
Manufactured for 73 USD, our device may be an economic alternative to
needle-based diagnostics. These results demonstrate the potential of
noninvasive ACS diagnostics to meet clinical standards and enhance patient
care.

急性间室综合征的诊断可通过非侵入式方法，使用皮肤上的力敏电阻器检测压力读数，最终通过蓝牙将诊断结果实时导出至 Web 应用程序。该诊断方法在预测准确度、精确度、敏感度和 F1 分数等关键性能指标上表现优异，其制造成本经济实惠，因此可能成为针刺诊断的经济替代品，并有潜力满足临床标准并提高患者护理水平。