Objective:To explore the clinical efficacy of a wearable robot for improving the balance and walking function of stroke survivors.Methods:Eighty stroke survivors were randomly divided into an observation group and a control group, each of 40. Both groups were given routine rehabilitation, but the observation group additionally received 20 minutes of training assisted by a wearable robot six days a week for 4 weeks. Before and after the experiment, both groups were evaluated using the Berg Balance Scale (BBS) and functional ambulation categories (FACs). Their movement distance and ellipse area were measured using a Prokin balance instrument, and their step length and pace on the affected side were recorded.Results:Significant improvement in the average BBS and FAC scores, exercise length, ellipse area, and step length and speed on the affected side was observed in both groups. On average, the experimental group′s results were significantly better than those of the control group.Conclusion:Supplementing conventional rehabilitation with wearable robot assistance can significantly improve the balance and walking function of stroke survivors.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.