Impact of JIA-Related Physiology on Machine Learning-Based Task Prediction Performance from Active Acoustics-Driven Achilles Tendon Sensing: A Proof-of-Concept Study

Luis G. Rosa, Quentin Goossens, Miguel Locsin, Lori A. Ponder, Sampath Prahalad, Omer T. Inan

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Keywords: Juvenile idiopathic arthritis (JIA), machine learning classification, non-invasive digital biomarker, pediatric rheumatology, vibration analysis

Abstract: Juvenile idiopathic arthritis (JIA) and its enthesitis related arthritis (ERA) subtype often present diagnostic challenges due to variable symptomatology and limited accessibility of advanced imaging. Here, we explore a non-invasive approach for diagnostic decision support using active vibrational sensing and machine learning to classify locomotion tasks to characterize symptomatology. By comparing classification performance across JIA subgroups, including ERA and its active/inactive states, we observed that ERA-related physiology reduces ML classifier accuracy, with sample comparison of ERA to the No ERA group yielding p = 0.002 and Cohen’s d effect size d = 2.05. Notably, the classification metrics’ performance was consistently higher on the No ERA group compared to the three ERA subgroups we considered, indicating that acoustic signatures from inflamed tendons and entheses modulate predictive performance. These findings suggest that task-based classification accuracy might serve as a surrogate biomarker for inflammation severity. Beyond presenting the methodology, ranging from data acquisition with a miniature vibration motor and accelerometer, to PCA-based feature extraction and multi-class classification, our results underscore the potential of integrating vibration sensing into clinical workflows. Ultimately, this study lays the groundwork for potentially enabling more robust, cost-effective diagnostic tools that could support early detection, monitoring, and personalized management of JIA and ERA.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Quentin Goossens, [e-mail]

Submission Number: 129