Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

BACKGROUND: Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms. METHODS: 54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated. RESULTS: The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%. CONCLUSIONS: Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Osteoradionecrosis of the upper cervical spine: MR imaging following radiotherapy for nasopharyngeal carcinoma.

PURPOSE: To document the MRI appearances of radiation-induced abnormalities in the cervical spine following treatment for nasopharyngeal carcinoma (NPC). METHODS: Patients with radiation-induced abnormalities in the upper cervical spine were identified from a retrospective analysis of reports from patients undergoing MRI follow-up. Imaging and clinical records of these patients were reviewed. Symmetrical distribution of abnormalities at C1 (anterior arch+/-adjacent aspect of the lateral masses) and C2 (dens+/-body especially with a characteristic horizontal rim of marrow preservation above the inferior endplate) were considered typical for osteoradionecrosis (ORN). RESULTS: Abnormalities of C1/2 were identified in 9/884 (1%) patients. The MRI distribution of abnormalities was typical for ORN in four and atypical in five patients. Abnormal soft tissue was present in the atlantoaxial joint in eight patients, forming a florid mass in six. This soft tissue was in direct continuity with the posterior nasopharyngeal wall ulceration via the retropharyngeal region. The final clinical diagnosis was ORN in eight, five of whom had clinical factors which suggested infection could have played a contributory role, and osteomyelitis in one patient. All patients had undergone additional radiotherapy treatment comprising of brachytherapy (7), stereotactic radiotherapy (1) or radiotherapy boost (2) and three had undergone nasopharyngectomy. CONCLUSION: ORN of the upper cervical spine following radiotherapy for NPC is more common than previously suspected and is seen in patients with additional treatment, especially brachytherapy. MRI features are often atypical and a contributory role of infection in the development of some cases of ORN is postulated.