A First View of the Effect of a Trial of Early Mobilization on the Muscle Strength and Activities of Daily Living in Mechanically Ventilated Patients With COVID-19

Objective: To retrospectively investigate the effect of early mobilization on the muscle strength and activities of daily living in patients with COVID-19 under mechanical ventilation. Design: This was a single-center, retrospective, observational study. Setting: Inpatient rehabilitation care in Japan. Participants: The study subjects were divided based on the onset of mobilization: under mechanical ventilation (n=17; aged 68.5±11.9, 13 male) and after extubation (n=11; aged 59.7±7.1, 6 male; N=28). Interventions: Mobilization, including dangle sitting, standing, walking, and muscle strengthening exercises. Main Outcome Measures: The outcome measures were Barthel Index, Medical Research Council Manual Muscle Test, and intensive care unit Mobility Scale. Results: The difference in the Barthel Index, Medical Research Council Manual Muscle Test, and intensive care unit Mobility Scale scores pre- and postintervention were not statistically significant between the 2 groups, but all significantly improved after the intervention. Conclusion: This small sample size study found no difference in the functional recovery of patients with severe COVID-19 who underwent early mobilization under mechanical ventilation relative to when it was begun after extubation

Effects of periodic robot rehabilitation using the Hybrid Assistive Limb for a year on gait function in chronic stroke patients

Using a robot for gait training in stroke patients has attracted attention for the last several decades. Previous studies reported positive effects of robot rehabilitation on gait function in the short term. However, the long-term effects of robot rehabilitation for stroke patients are still unclear. The purpose of the present study was to investigate the long-term effects of periodic gait training using the Hybrid Assistive Limb (HAL) on gait function in chronic stroke patients. Seven chronic stroke patients performed 8 gait training sessions using the HAL 3 times every few months. The maximal 10-m walk test and the 2-minute walking distance (2MWD) were measured before the first intervention and after the first, second, and third interventions. Gait speed, stride length, and cadence were calculated from the 10-m walk test. Repeated one-way analysis of variance showed a significant main effect on evaluation time of gait speed (F = 7.69, p < 0.01), 2MWD (F = 7.52, p < 0.01), stride length (F = 5.24, p < 0.01), and cadence (F = 8.43, p < 0.01). The effect sizes after the first, second, and third interventions compared to pre-intervention in gait speed (d = 0.39, 0.52, and 0.59) and 2MWD (d = 0.35, 0.46, and 0.57) showed a gradual improvement of gait function at every intervention. The results of the present study showed that gait function of chronic stroke patients improved over a year with periodic gait training using the HAL every few months

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates