Knee acoustic emissions as a noninvasive biomarker of articular health in patients with juvenile idiopathic arthritis: a clinical validation in an extended study population

• Type of Material: Article
• Authors: Quentin Goossens, Georgia Institute of Technology; Miguel Locsin, Emory University; Sevda Gharehbaghi, Georgia Institute of Technology; Priya Brito, Emory University; Emily Moise, Georgia Institute of Technology; Lori Ponder, Childrens Healthcare Atlanta; Omer T Inan, Georgia Institute of Technology; Sampath Prahalad, Emory University
• Language: English
• Date: 2023-06-20
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12969-023-00842-7
• Title of Journal or Parent Work: PEDIATRIC RHEUMATOLOGY
• Volume: 21
• Issue: 1
• Start Page: 59
• End Page: 59
• Grant/Funding Information: This work was supported by grants from the Patricia Bowman Terwilliger Foundation, the Marcus Foundation funds (to SP), the National Science Foundation Faculty Early Career Development Program (CAREER) Grant 1749677, Children’s Healthcare of Atlanta. ML and PB were partially supported by Rheumatology Research Foundation medical and graduate student preceptorship awards. QG was supported by a Fellowship of the Belgian American Educational Foundation.
• Keywords: Juvenile idiopathic arthritis; Digital Biomarker; THERAPY; Rheumatology; Life Sciences & Biomedicine; Pediatrics; Science & Technology; Supervised machine learning; Knee Joint Health; Joint acoustic emissions
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Background: Joint 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. Findings: A 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. Conclusion: Joint 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.