Active Vibrational Achilles Tendon Sensing for Identifying and Characterizing Inflammatory Symptomatology in Enthesitis Related Arthritis

• Published in: IEEE transactions on biomedical engineering Vol. 72; no. 2; pp. 645 - 654
• Main Authors: Goossens, Quentin, Locsin, Miguel, Ponder, Lori A., Chan, Michael, Ozmen, Goktug C., Prahalad, Sampath, Inan, Omer T.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Accelerometry - methods; Achilles tendon; Achilles Tendon - physiopathology; Adolescent; Algorithms; Ankle; Arthritis; Arthritis, Juvenile - diagnosis; Arthritis, Juvenile - physiopathology; Asymptomatic; Biomarkers; Child; Digital signatures; Diseases; Enthesopathy - diagnosis; Enthesopathy - physiopathology; Female; Humans; Inflammation; Juvenile idiopathic arthritis (JIA); Machine Learning; machine learning classification; Male; Medical services; Motor skill learning; non-invasive digital biomarker; pediatric rheumatology; Recording; Sensors; Signal Processing, Computer-Assisted; Tendons; Vibration; Vibration analysis; Vibration monitoring; Vibrations; Waveforms
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3466831

Objective : This study explores the potential of active vibrational sensing as a digital biomarker to identify and characterize inflammatory symptomatology in the Achilles tendon and its entheses in juvenile idiopathic arthritis (JIA), particularly enthesitis related arthritis (ERA), a subcategory of JIA. Methods: Active vibrational data were non-invasively recorded using a miniature coin vibration motor and accelerometer. Twenty active vibration recordings from children diagnosed with JIA were used in the analysis. Machine learning algorithms were leveraged to classify the vibrational signatures according to the corresponding subject groups. Subjects were classified into symptomatic ERA (sxERA), asymptomatic ERA (asxERA), and asymptomatic JIA (non-ERA) (asxNERA) groups based on clinical evaluations and ILAR criteria. Results: Distinct vibrational signatures were observed during tiptoe standing, providing differentiation between subject groups. Feature-based and waveform-based approaches effectively classified the sxERA group against asxNERA and asxERA groups using leave-one-subject-out (LOSO-CV) and 3-fold cross-validation. For the 3-fold cross-validation, the mean accuracies for distinguishing sxERA from asxNERA were 81% (feature-based) and 81% (waveform-based), while the accuracies for discriminating sxERA against asxERA were 73% (feature-based) and 74% (waveform-based). Conclusion: Active vibrational sensing demonstrates promise as a tool for identifying Achilles tendon inflammation in JIA, potentially aiding in early diagnosis and disease monitoring. Significance : Developing active vibrational sensing as a diagnostic modality could address challenges in diagnosing ERA and facilitate timely intervention and personalized care for JIA, potentially enhancing long-term patient outcomes.