Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest Machine Learning

TL;DR

This paper introduces a noninvasive, machine learning-based device using force-sensitive resistors to diagnose acute compartment syndrome accurately, offering a cost-effective alternative to invasive pressure measurements.

Contribution

It presents a novel noninvasive diagnostic method employing a random forest model with surrogate pressure readings, validated across motion and motionless scenarios.

Findings

Achieved up to 98% diagnostic accuracy.

Performed well in sensitivity and specificity metrics.

Maintained performance in motion scenarios.

Abstract

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 that can malfunction in motion settings. This study proposes an objective and noninvasive diagnostic for ACS. The device detects ACS through a random forest machine learning model that uses surrogate pressure readings from force-sensitive resistors (FSRs) placed on the skin. To validate the diagnostic, a data set containing FSR measurements and the corresponding simulated intracompartmental pressure was created for motion and motionless scenarios. The diagnostic achieved up to 98% accuracy. The device excelled in key performance metrics, including sensitivity and specificity, with a statistically insignificant performance difference in motion present cases. Manufactured for 73 USD, our device may be a cost-effective solution. These results demonstrate the potential of noninvasive ACS diagnostics to meet clinical accuracy standards in real world settings.