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Background: Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment. Objective: A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital. Methods: In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model. Results: We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%. Conclusion: A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities. [ABSTRACT FROM AUTHOR]

Background: Endovascular treatment for aneurysmal subarachnoid hemorrhage (aSAH) has high fatality and permanent disability rates. It remains unclear how the prognosis is determined by the complex interaction between clinical severity and aneurysm characteristics. Objective: This study aimed to design an accurate prognostic prediction model for aSAH patients after endovascular treatment and elucidate the interaction between clinical severity and aneurysm characteristics. Methods: We used a clinically homogeneous data set with 1029 aSAH patients who received endovascular treatment to develop prognostic models. Aneurysm characteristics were measured by variables, such as aneurysm size, neck size, and dome-to-neck ratio, while clinical severity on admission was measured by both comorbidities and neurological condition. In total, 18 clinical variables were used for prognostic prediction. Considering the imbalance between the favorable and the poor outcomes in this clinical population, both ensemble learning and deep reinforcement learning approaches were used for prediction. Results: The random forest (RF) model was selected as the best approach for the prognostic prediction for all patients and also for patients with good-grade aSAH. Using an independent test data set, the model made accurate predictions (AUC=0.869±0.036, sensitivity=0.709±0.087, specificity=0.805±0.034) with the clinical severity on admission as a leading contributor to the prediction. For patients with good-grade aSAH, the RF model performed the best (AUC=0.805±0.034, sensitivity=0.620±0.172, specificity=0.696±0.043) with aneurysm characteristics as leading contributors. The classic scoring systems failed in this patient group (AUC<0.600; sensitivity=0.000, specificity=1.000). Conclusion: The proposed prognostic prediction model outperformed the classic scoring systems for patients with aSAH after endovascular treatment, especially when the classic scoring systems failed to make any informative prediction for patients with good-grade aSAH, who constitute the majority group (79%) of this clinical population. [ABSTRACT FROM AUTHOR]

Purpose To compare the safety and efficiency of stent assisted coiling (SAC) with non-SAC for the management of ruptured intracranial aneurysms. Methods A meta-analysis that compared SAC with coiling alone and balloon assisted coiling was conducted by database searching. The primary outcomes of this study were immediate occlusion and progressive thrombosis rate, overall perioperative complication rate, and angiographic recurrence. Secondary outcomes included mortality at discharge, hemorrhagic and ischemic complications, and favorable clinical outcome at discharge and at follow-up. results eight retrospective cohort studies with 1408 ruptured intracranial aneurysms (SAC=499; non- SAC=909) were included. The SAC group tended to show a lower immediate complete occlusion rate than the non-SAC group (54.3% vs 64.2%; RR 0.90; 95% Ci 0.83 to 0.99; i2=17.4%) and achieved a significantly higher progressive complete rate at follow-up (73.4% vs 61.0%; RR 1.30; 95% Ci 1.16 to 1.46; i2=40.5%) and a lower recurrence rate (4.8% vs 16.6%; RR 0.28; 95% CI 0.16 to 0.50; i²=0.0%). with respect to safety concerns, overall perioperative complications in the SAC group were significantly higher (20.2% vs 13.1%; RR 1.70; 95% CI 1.36 to 2.11; i2=0.0%). However, no significant difference was found for mortality rate at discharge (6.3% vs 6.2%; RR 1.29; 95% CI 0.86 to 1.94; i²=0.0%), or favorable clinical outcome rate at discharge (73.4% vs 74.2%; RR 0.95; 95% CI 0.88 to 1.02; i²=12.1%) and at follow-up (85.6% vs 87.9%; RR 0.98; 95% CI 0.93 to 1.02; i²=0.0%; P=0.338). Conclusions SAC has a lower recurrence rate than non-SAC. Nevertheless, further validation by well designed prospective studies is warranted for determining whether stents improve angiographic outcome without an increased complication rate or unfavorable clinical outcome. [ABSTRACT FROM AUTHOR]