Stroke frequently results in upper limb motor dysfunction, with traditional therapies often failing to yield sufficient improvements. Emerging technologies such as virtual reality (VR) and noninvasive brain stimulation (NIBS) present promising new rehabilitation possibilities. This study systematically reviews and meta-analyses the effectiveness of VR and NIBS in improving upper limb motor function in stroke patients. Registered with PROSPERO (CRD42023494220) and adhering to the PRISMA guidelines, this study conducted a thorough search of databases including PubMed, MEDLINE, PEDro, REHABDATA, EMBASE, Web of Science, Cochrane, CNKI, Wanfang, and VIP from 2000 to December 1, 2023, to identify relevant studies. The inclusion criterion was stroke patients receiving combined VR and NIBS treatment, while exclusion criteria were studies with incomplete articles and data. The risk of bias was assessed using the Cochrane Collaboration tool. Statistical analysis was performed using Stata SE 15.0, employing either a fixed-effects model or a random-effects model based on the level of heterogeneity. A total of 11 studies involving 493 participants were included, showing a significant improvement in Fugl-Meyer Assessment Upper Extremity (FMA-UE) scores in the combined treatment group compared to the control group (SMD = 0.85, 95% CI [0.40, 1.31], p = 0.017). The Modified Ashworth Scale (MAS) scores significantly decreased (SMD = − 0.51, 95% CI [− 0.83, − 0.20], p = 0.032), the Modified Barthel Index (MBI) scores significantly increased (SMD = 0.97, 95% CI [0.76, 1.17], p = 0.004), and the Wolf Motor Function Test (WMFT) scores also significantly increased (SMD = 0.36, 95% CI [0.08, 0.64], p = 0.021). Subgroup analysis indicated that the duration of treatment influenced the outcomes in daily living activities. The combination of VR and NIBS demonstrates significant improvements in upper limb motor function in stroke patients. The duration of treatment plays a critical role in influencing the outcomes, particularly in activities of daily living. This systematic review has limitations, including language bias, unclear randomization descriptions, potential study omissions, and insufficient follow-up periods. Future studies should focus on exploring long-term effects and optimizing treatment duration to maximize the benefits of combined VR and NIBS therapy.

Background: Parkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.Objective: The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.Methods: Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.Results: The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.Conclusion: The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Background: Transcranial ultrasound stimulation (TUS) is a non-invasive neuromodulation technique with promising clinical potential. Its therapeutic efficacy and safety are significantly influenced by stimulation parameters. However, the global research hotspots and future research trends of TUS application in the field of rehabilitation are unclear. This study analyzes the status of TUS research. Understand the annual publication trends, international and institutional cooperation pattern and influential authors and journals and keyword hotspot.Methods: A comprehensive literature search was conducted on the Web of Science core database using TUS-related subject headings until 27 December 2024. Two researchers independently screened articles based on pre-determined inclusion and exclusion criteria. Software packages such as CiteSpace and VOSviewer were used to visualize the results.Results: A total of 577 literatures were included. The results show that the annual publication volume shows an increasing trend, reaching a peak in 2024. The United States, China and Germany dominated the number of publications, with the largest number of institutions being Harvard University, the University of Toronto and Brigham and Women’s Hospital. Brain stimulation is the journal with the most articles and citations. Research hotspots include transcranial magnetic stimulation, noninvasive brain stimulation, Parkinson’s disease, and Alzheimer’s disease.Conclusion: A bibliometric analysis of the literature shows that research interest in transcranial ultrasound stimulation is growing rapidly, with annual publications growing exponentially since 2013 and receiving increasing attention from researchers. The findings suggest that TUS is currently used primarily in neurological diseases, particularly in the study of Parkinson’s disease and Alzheimer’s disease. At the same time, it is found that an emerging international cooperation model with the partnership between the United States, China and Germany as the core has gradually taken shape. Although preclinical studies have shown promising neuromodulator effects, the current study suggests that TUS needs to undergo further multicenter clinical validation. These findings provide evidence to guide future research priorities for non-invasive neuromodulation.

Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for gait analysis and examine its ability to discriminate abnormal gait patterns. The repeatability tests of different raters and different days showed great consistency. Compared with the results measured by Vicon, gait velocity, cadence, step length, cycle time, and sagittal hip and knee joint angles measured by Odonate showed high consistency, while the consistency of the gait phase division and the sagittal ankle joint angle was slightly lower. In addition, the stages with statistical differences between healthy subjects and patients during a gait cycle measured by the two systems were consistent. In conclusion, Odonate has excellent inter/intra-rater reliability, and has strong validity in measuring some spatiotemporal parameters and the sagittal joint angles, except the gait phase division and the ankle joint angle. Odonate is comparable to Vicon in its ability to identify abnormal gait patterns in patients with post-stroke. Therefore, Odonate has the potential to provide accessible and objective measurements for clinical gait assessment.

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

Objective: This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.Methods: Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.Results: The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.Conclusion: These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.