Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

• Published in: Frontiers in digital health Vol. 2; p. 571839
• Main Authors: Whittingslow, Daniel C, Zia, Jonathan, Gharehbaghi, Sevda, Gergely, Talia, Ponder, Lori A, Prahalad, Sampath, Inan, Omer T
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.11.2020
• Subjects: acoustic sensing; Digital Health; juvenile idiopathic arthiritis; machine learning; signal processing; wearable sensors; acoustic sensing; wearable sensors; machine learning; signal processing; juvenile idiopathic arthiritis
• ISSN: 2673-253X; 2673-253X
• DOI: 10.3389/fdgth.2020.571839

In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA-10 of whom were recorded a second time 3-6 months later-and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA.