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Abstract BackgroundJoint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. FindingsA total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. ConclusionJoint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy. 

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.