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
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Indocyanine green (ICG) fluorescence angiography is an emerging technique that can provide detailed anatomical information during surgery. The purpose of this study is to determine whether ICG fluorescence angiography can be used to evaluate the blood flow of the rotator cuff tendon in the clinical setting. Twenty-six patients were evaluated from October 2016 to December 2017. The participants were categorized into three groups based on their diagnoses: the rotator cuff tear group; normal rotator cuff group; and adhesive capsulitis group. After establishing a posterior standard viewing portal, intravenous administration of ICG at 0.2 mg/kg body weight was performed, and fluorescence images were recorded. The time from injection of the drug to the beginning of enhancement of the observed area was measured. The hypovascular area in the rotator cuff was evaluated, and the ratio of the hypovascular area to the anterolateral area of the rotator cuff tendon was calculated (hypovascular area ratio).Objectives
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