RESUMO
BACKGROUND: Noninvasive ultrasound (US) has been used therapeutically for decades, with applications in tissue ablation, lithotripsy, and physical therapy. There is increasing evidence that low intensity US stimulation of organs can alter physiological and clinical outcomes for treatment of health disorders including rheumatoid arthritis and diabetes. One major translational challenge is designing portable and reliable US devices that can be used by patients in their homes, with automated features to detect rib location and aid in efficient transmission of energy to organs of interest. This feasibility study aimed to assess efficacy in rib bone detection without conventional imaging, using a single channel US pitch-catch technique integrated into an US therapy device to detect pulsed US reflections from ribs. METHODS: In 20 healthy volunteers, the location of the ribs and spleen were identified using a diagnostic US imaging system. Reflected ultrasound signals were recorded at five positions over the spleen and adjacent ribs using the therapy device. Signals were classified as between ribs (intercostal), partially over a rib, or fully over a rib using four models: threshold-based time domain classification, threshold-based frequency domain classification, logistic regression, and support vector machine (SVM). RESULTS: SVM performed best overall on the All Participants cohort with accuracy up to 96.25%. All models' accuracies were improved by separating participants into two cohorts based on Body Mass Index (BMI) and re-fitting each model. After separation into Low BMI and High BMI cohorts, a simple time-thresholding approach achieved accuracies up to 100% and 93.75%, respectively. CONCLUSION: These results demonstrate that US reflection signal classification can accurately provide low complexity, real-time automated onboard rib detection and user feedback to advance at-home therapeutic US delivery.
RESUMO
OBJECTIVE: Rheumatoid arthritis (RA) is a chronic inflammatory syndrome that features painful and destructive joint disease. Aggressive disease-modifying treatment can result in reduced symptoms and protection from irreversible joint damage; however, assessment of treatment efficacy is currently based largely on subjective measures of patient and physician impressions. In this work, we address this compelling need to provide an accurate and quantitative capability for monitoring joint health in patients with RA. METHODS: Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics were measured noninvasively from 11 patients with RA over the course of three weeks using a custom multimodal sensing brace, resulting in 49 visits with JAE recordings and 43 with EBI recordings. Features derived from all sensing modalities were fed into a linear discriminant analysis (LDA) model to predict disease activity according to the validated disease activity index (the DAS28-ESR). Erythrocyte sedimentation rate (ESR) was predicted using ridge regression and classified into a high or low class using LDA. RESULTS: DAS28-ESR level was predicted with an area under the receiver operating characteristic curve (AUC) of 0.82. With JAEs alone, we were able to track intrasubject differences in the disease activity score as well as classify ESR level with an AUC of 0.93. The majority of patients reported both an interest and ability to use the brace at home for longitudinal monitoring. CONCLUSION: This work demonstrates the ability to detect RA disease activity using noninvasive sensing. SIGNIFICANCE: This system has the potential to improve RA disease activity monitoring by giving treating clinicians objective data that can be acquired independent of a face-to-face clinic visit.