Multimodal Wearable System for Objective Assessment of Dynamic Rotational Knee Biomechanics Following ACL Injury and Reconstruction: A Clinical Validation Study Using Ensemble Deep Learning
Abstract
ABSTRACT:
Background: The clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability.
Objective: The purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction.
Methods: This prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed ≥6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8…
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