An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
Methods
This study aimed to define the histopathology of degenerated humeral head cartilage and synovial inflammation of the glenohumeral joint in patients with omarthrosis (OmA) and cuff tear arthropathy (CTA). Additionally, the potential of immunohistochemical tissue biomarkers in reflecting the degeneration status of humeral head cartilage was evaluated. Specimens of the humeral head and synovial tissue from 12 patients with OmA, seven patients with CTA, and four body donors were processed histologically for examination using different histopathological scores. Osteochondral sections were immunohistochemically stained for collagen type I, collagen type II, collagen neoepitope C1,2C, collagen type X, and osteocalcin, prior to semiquantitative analysis. Matrix metalloproteinase (MMP)-1, MMP-3, and MMP-13 levels were analyzed in synovial fluid using enzyme-linked immunosorbent assay (ELISA).Aims
Methods
