Search Results (555)

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background/Objectives: White–Sutton syndrome (WHSUS; OMIM 616364) is a rare neurodevelopmental disorder caused by pathogenic variants in the POGZ gene and characterized by developmental delay, intellectual disability, speech impairment, autism spectrum features, and dysmorphic traits. Although most reported cases are sporadic, inherited forms are exceptionally rare. We describe a familial case of WHSUS involving an affected mother and two children carrying a heterozygous POGZ nonsense variant, highlighting marked intra-familial phenotypic variability and expanding the clinical spectrum of the disorder. Methods: Clinical evaluation included multidisciplinary assessments. Genetic testing was performed using clinical exome sequencing (CES) with a virtual neurodevelopmental disorder (NDD) gene panel, followed by Sanger confirmation and segregation analysis in family members. The POGZ transcript reference NM_015100.3 was used for variant nomenclature and verified with the Mutalyzer tool. CNV detection from NGS data was performed using the Alissa CNV caller (Agilent) and visualized via IGV; the Xp11.22 microduplication was confirmed by chromosomal microarray (aCGH) and parental segregation analyses. Results: CES identified the heterozygous pathogenic POGZ variant c.1522C>T (p.Arg508*) in the female proband (III₆), an infant presenting with global developmental delay, hypotonia, speech impairment, gait abnormalities, and characteristic dysmorphic features. Segregation analysis demonstrated maternal inheritance and confirmed the presence of the variant in her affected brother (III₄), who also carries a de novo 1.79 kb microduplication at Xp11.22, while the maternal grandparents tested negative, indicating a de novo origin in the mother. The mother exhibited an attenuated phenotype, including mild neuropsychiatric and gastrointestinal manifestations. The variant is predicted to undergo nonsense-mediated decay (NMD), consistent with a moderate clinical presentation; however, experimental validation was not performed. Conclusions: This report documents a rare familial occurrence of WHSUS with highly variable expressivity. Our findings broaden the phenotypic and molecular characterization of POGZ-related disorders and emphasize the importance of comprehensive segregation studies and early genomic diagnosis. While experimental data link POGZ deficiency to DNA repair defects, no longitudinal clinical studies have demonstrated increased cancer risk in WHSUS; therefore, formal malignancy screening guidelines cannot be established at present, and this issue deserves future study in larger cohorts or registries. Full article

Background: Patients undergoing open-heart surgery (OHS) are at risk of postoperative morbidity and mortality. Physical performance has been increasingly recognized as an important factor influencing postoperative outcomes. Therefore, the study aimed to investigate the associations and predictive value of physical performance on postoperative complications and duration of hospital stay. Methods: A prospective cohort study was conducted in 116 patients who were admitted to OHS. Preoperative assessment of physical performance, i.e., Short Physical Performance Battery (SPPB), Five Times Sit to Stand Test (5STS), gait speed (5 m walk test: 5MWT), Timed Up and Go (TUG), and handgrip strength. Duration of hospital stay and incidence of post-operative complications were recorded. Differences between participants with and without postoperative complications were analyzed using independent samples t-tests for continuous variables and chi-square tests for categorical variables. The associations between physical performance and postoperative outcomes were assessed using Spearman’s rank correlation coefficient. Hierarchical regression analysis was conducted to determine the predictive contribution of physical performance. Results: A total of 116 participants were submitted for OHS in two medical school hospitals; however, 108 individuals completed the pre-operative physical performance. The most common procedures were coronary artery bypass grafting and valve surgery. Fifty-one participants (47.22%) experienced postoperative complications, including five deaths, corresponding to 4.63% mortality. For the length of hospital stay analysis, five participants who died postoperatively were excluded, resulting in a final sample of 103 participants. Physical performance was significantly associated with the length of hospital stay (p < 0.05). Hierarchical regression analysis showed that the final prediction model explained 13.4% of the variance in length of hospital stay, with SPPB independently contributing an additional 6.0% to the model, followed by 5STS, 5MWT, handgrip strength, and TUG, which accounted for an additional 5.1%, 4.6%, 4.4%, and 3.7%, respectively. Conclusions: Preoperative physical performance was associated with length of hospital stay. While each measure explained a relatively small proportion of the variance in hospital stay, these assessments offer a simple, non-invasive, and clinically feasible approach to evaluating functional reserve before surgery. These findings highlight the importance of incorporating functional assessment into perioperative care to support risk stratification and guide rehabilitation strategies. Full article

The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student autonomy and efficiency. However, their actual impact on academic performance remains understudied, and the existing research often presents contradictory findings. To address this gap, the present study is the first to employ a Genetic Algorithm (GA) and Multi-Layer Perceptron Neural Networks (MLPNNs) to evaluate the influence of Generative AI Tools (GAITs) on students’ academic outcomes. A structured questionnaire was administered to 294 students from three Moroccan engineering schools in order to collect data on their use of these tools. An initial attempt to predict their grades using a statistical approach showed that familiarity with GAITs contributed positively to academic performance but had limited accuracy (39%), highlighting the need for more robust methods. Therefore, a hybrid model based on neural networks optimized with a GA was developed to better capture the complex relationships between the explanatory variables and academic performance. The results indicate that the GAIT-related variables considered in this study, taken in isolation, have a limited predictive capacity for students’ academic outcomes. This finding suggests that the available data does not capture the full complexity of the factors shaping academic success in contexts involving GAITs use. Full article

Older adults with mild cognitive impairment (MCI) often show gait impairment during dual-task walking and obstacle negotiation. This assessor-blinded randomized controlled trial investigated whether dual-task gait training with visual adaptation, added to a general exercise program, improves gait and related functional outcomes in older adults with MCI. Forty participants aged 65 years or older who met the MCI criteria were randomly allocated to a dual-task gait training with visual adaptation group or a control group (n = 20 each). Spatiotemporal and adaptive gait parameters were assessed before and after 4 weeks of intervention during level walking and during predictable and unpredictable obstacle negotiation under light and noise conditions. Balance, executive function, and concern about falling were also evaluated. Compared with the control group, the intervention group showed greater improvements in level walking and predictable obstacle negotiation, including longer step and stride length, shorter step and stride time, higher cadence, and faster gait speed. Under unpredictable obstacle conditions, gains were more selective and were observed mainly in step and stride length and adaptive gait indices. The intervention group also showed greater improvement in balance and executive function and a larger reduction in concern about falling. These findings suggest that adding dual-task gait training with visual adaptation to a general exercise program may have clinical value for improving adaptive gait and related functional outcomes in older adults with MCI. However, because the intervention group received additional gait-specific training and a higher total training dose than the control group, future dose-matched studies are needed to clarify the specific contribution of visual adaptation. Full article

Background: Hip implant fractures are rare, yet difficult to correct once they occur. For cemented implants, fracture is often associated with increased stresses at the implant stem when proximal regions of the implant have debonded. While deterministic analyses offer predictive power by using averages for model inputs, averages fail to capture the variability inherent in device manufacturing and musculoskeletal biology. This study developed a probabilistic finite element model of a debonded hip implant to better account for some of these variabilities to predict the most likely failure mode. The hypothesis was that fatigue would be more likely to occur than overloading. Methods and Materials: Monte Carlo sampling generated 1000 simulations varying the material elastic modulus (implant, cement, and bone) and loading magnitude at stance phase of the gait. The resultant distributions of maximum von Mises stress at the stem were compared to distributions for failure properties in the literature. Results: The analysis found the likelihood of the implant failing due to overloading was remote. In contrast, fatigue failure had a 99.4% chance of occurring. Fracture mechanics predicted that the debonded implant would reach critical flaw length between 1.8 and 26.4 months, with a mean of 7.2 months. Conclusions: The results show good agreement with the findings of the case study the model was based on, particularly in predicting the location of failure and fatigue life. The results of this study provide a framework for developing future decision-making tools that ultimately may assist clinicians in deciding when interventions are necessary to minimize the risk of implant or periprosthetic fracture. Full article

Preliminary estimation of lower-limb motor function is important in rehabilitation research, especially for biomechanical assessment of post-stroke hemiplegic gait. However, subject-specific musculoskeletal modeling in this population is challenging because standard maximum voluntary contraction (MVC) testing is often unsafe or unreliable for normalizing surface electromyography (sEMG) signals. To address this limitation, a normalized correction coefficient was introduced for pathological sEMG preprocessing, and an improved Hill-type muscle model (iHMM) was established to account for submaximal activation conditions. By combining inverse dynamics, static optimization, and a subject-specific lower-limb dynamic model, the proposed framework was used to estimate musculotendon force, knee joint torque, knee joint kinematics, and shank center-of-mass trajectory. In a preliminary validation involving six hemiplegic subjects, the predicted knee joint torques showed moderate to good agreement with the reference results, with correlation coefficients ranging from 0.724 to 0.807 and RMSE values ranging from 3.872 to 7.814 Nm. These preliminary results support the feasibility of the proposed framework for subject-specific biomechanical analysis of the hemiplegic lower extremity and suggest its potential utility in individualized rehabilitation assessment. Full article

Continuous estimation of oxygen uptake (VO₂) using wearable inertial sensors offers a practical alternative to laboratory-based metabolic testing but remains challenging due to the indirect relationship between kinematics and physiological demand. This study presents a lightweight two-stage pipeline for simultaneous heel-strike (HS) detection and VO₂ estimation using a single calf-mounted IMU. In Stage 1, an Extreme Learning Machine (ELM) + Random Forest (RF) ensemble achieves the highest HS detection F1-score (0.818) under leave-one-subject-out (LOSO) validation, outperforming a temporal convolutional network (TCN) deep learning baseline (F1 = 0.674), which exhibited higher variability across subjects. In Stage 2, kinematic and gait-derived features from 30 s windows are used to estimate normalized VO₂ via RF and ensemble regression under LOSO cross-validation across 24 participants. The RF model achieves a median R² of 0.687 using predicted HS (Pred-HS) events and 0.679 using ground-truth (GT) annotations, with the ensemble showing similar performance (median R² ≈ 0.675–0.691). No statistically significant difference was observed between GT-HS and Pred-HS conditions (p > 0.05). SHAP analysis identifies accelerometer variability (acc_std) and gyroscope-derived features as dominant predictors, with demographic variables contributing minimally. Overall, the results suggest that VO₂ estimation may be achieved using automatically detected gait events without manual annotation. The proposed pipeline is computationally efficient and indicates feasibility under controlled conditions, subject to further validation. Full article

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

Background/Objectives: Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that increases the risk for cardiovascular disease. Although gait impairments are documented in older adults with MetS, few studies have examined gait biomechanics or the potential for gait-related measures to differentiate metabolic syndrome status in young adults. This study examined whether gait biomechanics, functional gait performance, and muscle strength are associated with MetS risk factors in young adults, and whether these measures predict MetS classification. Methods: Twenty-four young adults meeting criteria for metabolic syndrome (MetS+) and 24 participants without MetS (MetS−) completed cardiometabolic assessments, gait analysis, functional gait testing, and lower extremity isometric strength testing. Multiple linear regression examined associations between gait velocity and MetS risk factors, and binary logistic regression assessed the ability of biomechanical, functional, and strength variables to differentiate MetS status. Results: Compared with matched controls, MetS+ participants demonstrated slower gait velocity, longer stance time, and lower propulsive ground reaction forces. Regression models examining MetS risk factors did not significantly explain variance in gait velocity. Logistic regression indicated that spatiotemporal gait parameters and GRF variables could differentiate MetS classification with fair predictive ability, whereas functional gait performance and strength measures showed limited classification performance. Conclusions: Young adults with MetS demonstrated modest differences in select gait variables, but the MetS risk factors did not show strong relationships with gait velocity in regression analyses. Spatiotemporal gait parameters differentiated MetS+ and MetS− groups but offered limited predictive value. These findings suggest that subtle biomechanical differences may be present early in the progression of MetS, although stronger functional impairments may not yet be detectable in young adults. Full article

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

Bone resorption secondary to stress shielding is a leading cause of hip implant failure, primarily due to the stiffness mismatch between the femur and the prosthesis. Although anatomical stem designs generally provide improved load transfer, Dorr type C femurs often require straight stems to ensure adequate primary stability. This work presents a systematic approach to designing a straight, additively manufactured porous titanium hip stem aimed at minimizing stress shielding. The lattice architecture is customized to replicate the mechanical properties of bone based on patient-specific femoral CT scans. The performance of the resulting porous implant is numerically assessed under simplified physiological gait loading conditions. The implant behavior is evaluated through a homogenization strategy to model the lattice structure, significantly reducing the computational effort and making the methodology easily replicable. Compared to its full counterpart, the porous design achieves a significant reduction in predicted bone loss, suggesting that the proposed framework is a promising proof of concept for patient-specific implants. While further experimental validation and larger cohort studies are required, these findings highlight the potential of mechanically tunable porous structures to mitigate the stress shielding phenomenon in anatomical conditions such as Dorr type C femurs, which require straight stems. Full article

The evaluation of biomechanical parameters in four-beat gaited horses remains limited by the subjectiveness and complexity of current standard methods. Through a deep learning approach, we aimed to infer dissociation % using only acoustic signals. A total of 268 audio samples were extracted from publicly available videos featuring three Brazilian horse breeds (Mangalarga Marchador, Campolina, and Piquira) performing marcha batida and marcha picada. Acoustic features, including root mean square energy (RMS), zero-crossing rate (ZCR), and 13 Mel-frequency cepstral coefficients (MFCCs), were extracted and used to train a long short-term memory (LSTM) neural network. The model accurately predicted the time intervals between successive hoof–ground contacts (R² = 0.98; MAE = 0.0071), enabling the calculation of the dissociation %. While no significant differences were found between gait types and dissociation %, breed-related differences in both mean hoof–ground contact interval and dissociation were observed, with 8 acoustic features demonstrating discriminative power. Our results suggest that hoof–ground contact patterns can be quantified objectively from audio alone, offering a practical and non-invasive method for gait analysis. The approach holds potential for applications in breed standardization, selection, and digital locomotion phenotyping of horse populations. Full article