Technology and Health Care

Cardiovascular disease, especially heart failure, is a substantial global health issue. By integrating PHR with machine learning, early disease detection could be made possible.

In this study, an attempt was made to develop and fine-tune an AI model that would forecast the likelihood of heart failure based on patient data in PHRs.

Data from 1025 patients and 12 clinical/demographic criteria were used. An untuned multilayer perceptron (MLP) with two hidden layers (10 and 5 neurons) was first trained (1000 epochs). Then, using the same dataset, we performed systematic hyperparameter tuning (grid search with 5–fold cross–validation) for Logistic Regression, Random Forest, SVM, and an enhanced MLP. Performance metrics included accuracy, precision, recall, F1–score, MCC, and ROC–AUC with 95% confidence intervals.

The original untuned MLP gave a mean accuracy of 0.7244 (±0.0245) and mean ROC–AUC of 0.724 (±0.038). After tuning, Random Forest achieved the highest performance (AUC = 0.959, 95% CI 0.924–0.986; accuracy = 0.890). The tuned MLP reached AUC = 0.830 (CI 0.763–0.893), outperforming the untuned version and showing comparable performance to Logistic Regression (AUC = 0.824) and SVM (AUC = 0.842).

These results suggest potential use of AI models to anticipate the risk of heart failure from a subject's medical history and provide an avenue toward scalable and personal medicine, resulting in improved early prevention and treatment of cardiovascular disease.

The rapid advancement of digital technologies has transformed healthcare delivery, yet significant gaps remain in patient-centred research on digital healthcare services (DHCS).

This study aims to map the global research landscape of patient-centred DHCS, identify key themes and collaboration patterns, and highlight gaps to guide future research.

Following a narrative review of DHCS evolution with China as a regional case study, a bibliometric analysis was conducted on 4163 publications retrieved from Scopus (2020–2025). VOSviewer and the Bibliometrix R package were used for visual mapping and trend analysis.

Patient satisfaction with telemedicine has emerged as a key research focus, while on-site healthcare scenarios receive comparatively less attention. Additionally, the elderly population, as primary users of healthcare services, remains underrepresented in studies on the usability and experience of digital healthcare services. Research methodologies, including questionnaires, interviews, and randomised controlled trials, address diverse demographics and dimensions. International collaboration is dominated by the United States and European countries, reflecting their scientific influence.

Critical gaps identified include insufficient attention to on-site DHCS and elderly users, alongside disparities in global research influence. Future research should prioritise these areas and strengthen cross-continental collaboration to advance patient-centred digital healthcare delivery.

In this retrospective cohort study, data from 1974 ischemic stroke patients were extracted from the MIMIC-IV database. Patients were randomly allocated into a training set (n = 1582) and a test set (n = 392) in an 8:2 ratio. Five machine learning algorithms (CART, RF, SVM, GBM, and NB) were employed for initial feature screening. Key predictors were subsequently integrated into a multivariate logistic regression model, which was visualized as a nomogram. The model's performance was evaluated using discrimination, calibration, and clinical utility metrics and was compared against established severity scores (SOFA, SAPS II, LODS, OASIS, GCS).

The final nomogram incorporated nine predictors: age, heart rate, weight, glucose, anion gap, calcium, alkaline phosphatase (ALP), red cell distribution width (RDW), and mean corpuscular hemoglobin concentration (MCHC). The model demonstrated an AUC of 0.739 (95% CI: 0.715–0.764) in the training set and 0.737 (95% CI: 0.688–0.786) in the test set. Calibration curves indicated good agreement between predictions and observations. Decision curve and clinical impact curve analyses confirmed the nomogram's favorable clinical net benefit, and it outperformed all comparator scoring systems in discriminatory ability.

We developed and validated a practical nomogram that effectively integrates key clinical variables to predict one-year mortality risk in ICU patients with ischemic stroke. This tool demonstrates robust performance and potential clinical utility for risk stratification.

Effective leadership is crucial for delivering high-quality healthcare, optimizing employee performance, and improving patient outcomes.

This study conducts a bibliometric analysis to examine leadership styles in the healthcare sector, identifying key research trends, influential works, and their impact on work outcomes.

Using Scopus-indexed publications (2010–2024), this study analyzes eighty-three relevant documents with VOSviewer.

The findings reveal that various leadership styles, such as transformational, transactional, authentic, collaborative, inclusive, servant, and paternalistic leadership, have a significant impact on both individual and organizational outcomes. Furthermore, transformational leadership is the most studied and influential style, promoting employee engagement, innovation, and better patient care. Transactional leadership ensures structure and efficiency but offers limited long-term benefits. Servant leadership fosters trust, collaboration, and ethical decision-making, while authentic leadership enhances transparency and psychological safety. Additionally, the study highlights a growing focus on healthcare leadership, with Medicine and Business, Management & Accounting as the dominant disciplines.

This bibliometric study highlights the critical role of leadership styles in shaping work outcomes in the healthcare sector. Transformational leadership emerges as the most influential, fostering innovation, engagement, and improved patient care.

The classification of electrocardiogram (ECG) signals is a critical task in detecting cardiac arrhythmias. However, challenges such as class imbalance and the need to capture both local and global temporal patterns make this problem complex.

In this study, a hybrid deep learning model, called MBViT-FocalGA, is proposed for the classification of electrocardiogram ECG signals.

The proposed model combines a Multi-branch Vision Transformer (MBViT) architecture with a Focal Loss function optimized via a Genetic Algorithm (GA) called as MBViT-FocalGA. Thanks to three independent ViT branches operating at different patch sizes, the model can effectively learn both local and global timing patterns from ECG signals. Furthermore, the class imbalance problem frequently encountered in arrhythmia data is mitigated by optimizing the hyperparameters of the Focal Loss function with GA, improving the recognition of minority classes. The model's performance was evaluated on the MIT-BIH Arrhythmia Database in two different scenarios: (i) a two-class scenario combined into normal (N) and others (O), and (ii) a five-class scenario consisting of N, L, R, A, and V.

Experimental results demonstrate that the MBViT-FocalGA model achieves superior classification performance with 98% overall accuracy and a high F1 score, outperforming classical deep learning models for both scenarios.

These findings demonstrate the strong potential of transformer-based architectures and adaptive loss function optimizations in ECG classification.

An accurate diagnosis of children's self-care problems significantly matters in the growth and development of children. However, various and extensive disorders make the self-care problems classification extremely complex and require much effort and time to solve.

To deal with the above challenge, a deep learning model is proposed to classify the children's self-care problems intelligently and precisely.

The proposed deep learning model contains two sub-deep neural networks. The first sub-network employs a technology of representing learning named triplet loss. It aims to compress the dimensions of the feature of the children with self-care problems to extract the useful information and exclude the noise, in order to improve classification performance. The second sub-network utilizes a technology for handling the class imbalance problem called focal loss to further improve the classification accuracy.

The experimental results show that the proposed deep learning model outperforms. The averages of accuracy, precision, recall, and F1 score can achieve 99.78%, 0.99, 0.99, and 0.99, respectively.

To the best of our knowledge, the proposed method achieves state-of-the-art results. That can significantly support the rehabilitation and growth of children with self-care issues. Furthermore, this study also provides a demonstration and experience of the application of the deep learning model in the healthcare field.

During coronary artery bypass grafting(CABG), surgeons commonly use transit time flow meter (TTFM) to determine the graft patency. This study is to explore the effect of a special “spike waveform” in CABG on graft patency and its hemodynamic environment.

We collected the data of 1154 patients undergoing CABG, including intraoperative TTFM waveform and coronary CTA at 1 week after surgery. 239 patients had 1-year follow-up CTA. We divided the grafts into spike and non-spike waveform groups, and assessed the patency of grafts. Additionally, we constructed an ideal model of CABG, we also calculated and extracted the hemodynamic parameters of the grafts.

For immediate patency, the occlusion rates of spike and non-spike waveforms of left internal mammary artery(LIMA) were 0 and 5.13%, those of saphenous vein graft(SVG) were 8.59% and 5.79%. As for medium-long term patency, the occlusion rates of spike and non-spike waveforms were 0 and 12.56% for LIMA, and 42.47% and 16.84% for SVG. Regarding hemodynamics, the relative residence time(RRT) and Max Oscillating shear index(MaxOSI) of the LIMA spike waveform were significantly higher than those of the non-spike waveform, and the mean OSI and MaxOSI of the SVG spike waveform were substantially higher than those of the non-spike waveform.

Spike waveform slightly reduces the immediate patency of SVG and significantly reduces the medium-long term patency, while no effect on the patency of LIMA is found. In terms of hemodynamics, both LIMA and SVG with spike waveforms exhibit unfavorable hemodynamic conditions within the grafts.

Clinical fall risk prediction often relies on subjective observation or simplistic metrics, despite the high costs associated with falls in older adults.

This proof-of-concept study evaluated the validity and reliability of a consumer-grade depth camera system as an objective alternative for automated fall risk assessment.

Thirty-nine community-dwelling adults performed Timed Up and Go (TUG), Five Times Sit-to-Stand (FTSS), and Tandem Stance (TST) tests. Concurrent measurements were taken by an automated depth camera and blinded physical therapists. Validity (concurrent, convergent, discriminative) and reliability were assessed.

Automated FTSS and TUG tests demonstrated strong concurrent validity with therapist measurements (r = 0.813 and 0.915) and high discriminative accuracy for fall history (AUC = 0.941 and 0.864). Depth camera-based FTSS vertical velocity was significantly lower in participants with a fall history (p < 0.001). TST sway metrics showed limited discriminative validity. The system showed good to excellent test-retest reliability. In an age-stratified analysis of older adults (≥65 years), AFTSS time and the AUC-weighted composite score demonstrated acceptable discrimination for retrospective fall history (AUC = 0.892 and 0.867, respectively)

The depth camera system showed promise as a valid and reliable tool for objective quantification of performance on fall-risk-related functional tests, particularly FTSS and TUG, when benchmarked against therapist-administered measurements. Discriminative findings against retrospective fall history should be interpreted as exploratory, and larger prospective studies are required before clinical screening thresholds can be recommended.

ClinicalTrials.gov (NCT06519864).

Conventional surgical instruments made from Stainless steel (SS), Titanium (Ti), and Tantalum (Ta) are widely utilized because of their excellent corrosion resistance and strength-to-weight ratio. However, these materials lack antibacterial properties, which could increase the risk of surgical site infections. Recent advancements in antimicrobial and biocompatible coatings, particularly Ag-Ta₂O₅ (Silver-Tantalum Pentoxide), present better solutions for new surgical instrument design. Despite numerous studies published on this topic, a comprehensive review specifically addressing Ag-Ta₂O₅ coatings remains absent, resulting in fragmented information across the literature. Systematizing this information and consolidating the developments in this field are beneficial, and this is the motivation behind this review.

For these reasons, this review examines the development and applications of these advanced coatings, with a focus on their antimicrobial and biocompatible properties.

We critically examine the technological challenges, innovative coating methodologies, and the comparative advantages of Ag-Ta₂O₅ over traditional and hybrid coatings.

Furthermore, this review identifies future research directions and proposes strategic collaborations among clinicians, engineers, policymakers, and materials scientists to expedite the clinical adoption of these coatings.

It is envisioned that this review paper will serve as a valuable source of information for engineers, researchers, and clinicians to stay current with the latest developments in this area and for new researchers to initiate their exploration of coating technology.

Airway opening is crucial in management, and failure can lead to respiratory failure and death. Current manual methods have high failure rates, our team developed a device that automatically and precisely opens the patient's airway, replacing manual techniques.

To explore whether a new airway opening device with automatic position adjustment can effectively shorten the time for the tracheoscope to enter the airway while maintaining the airway open compared with the manual method of opening the airway.

A multicenter, prospective, randomized controlled trial involved 400 adult patients undergoing bronchoscopy in three tertiary hospitals in China. Patients were randomly assigned to a control group using the head-up jaw lift method or to instrument test groups using the device at an angle of position (PA) of 90°, 95°, and 100°.

A total of 357 patients completed the trial with a 100% success rate of bronchoscope entry. Median entry times for tracheoscope to enter the airway were 39.00 s for the control group, 33.00 s for PA 90° (P = 0.007), 31.00 s for PA 95° (P = 0.004), and 32.00 s for PA 100° (P = 0.005).

The new device can safely and effectively open the airway, shorten tracheoscope entry time, and maintain an open airway accurately.

In recent years, for patients with arterial stenosis up to 75% and poor medical control, stent implantation has become the mainstream choice.

The purpose of this work is to study the effects of stent implantation on hemodynamics in the stenotic artery considering microcirculation.

A stent implantation model with microcirculation is constructed. Expansion simulations are carried out for the grid stent and the link stent respectively to analyze the changes in hemodynamic parameters and the mechanical responses of the plaque and the stent.

Compared with the stent implantation study without microcirculation, the results show that the pressure gradient decreases, and the probability of high time averaged wall shear stress near the stent wires reduces after the grid stent is implanted and expanded. The link stent generates time averaged wall shear stress close to the normal physiological range, and no regions with high oscillatory shear index and high relative residence time are found near the stent wire upstream of the stent.

The link stent can not only better improve the hemodynamic environment, but also reduce the risk of intimal hyperplasia and atherosclerosis. These findings reflect the importance of microcirculation in the regulation of hemodynamics after different stent implantations, providing a new perspective for optimizing stent design.

Artificial intelligence (AI) shows considerable potential for sports injury prediction, yet a comprehensive methodological review of its empirical applications remains limited. This study aimed to systematically review the empirical literature on the use of AI and machine learning (ML) for sports injury prediction.

Following the PRISMA 2020 guidelines, a systematic search was conducted across PubMed, IEEE Xplore, SPORTDiscus, Web of Science, and Scopus for literature published between January 2015 and March 2026. In addition to the structured database search, a small number of database-recommended articles identified through platform recommendation functions were also screened for eligibility. After a multi-stage screening process, 18 empirical studies were included in the final qualitative synthesis.

Risk of bias assessment using PROBAST indicated that only one study (5.6%) had a low overall risk of bias, whereas most studies were judged to be high risk, primarily because of weaknesses in the analysis domain and reliance on internal validation. Across the included studies, AI-based models demonstrated potential for handling multidimensional training load, physiological, biomechanical, and psychological data; however, most prediction models relied exclusively on internal validation, limiting confidence in their generalizability.

AI demonstrates clear promise for sports injury prediction, but the current evidence base remains constrained by limited external validation, inconsistent reporting practices, and the continued overrepresentation of male cohorts. Future research should prioritize methodological rigor, broader geographic and demographic representation, standardized reporting, and explainable AI (XAI) approaches to enhance the trustworthiness and practical utility of these models for coaches and clinicians.

Recognizing that walking is a critical marker of independence, fall risk, and overall health in adults with intellectual disability (ID) and that comparative evidence by Down syndrome (DS) status remains limited, this study aimed to quantify spatial, temporal, and kinematic gait differences across flat and compliant surfaces in adults with and without DS and to evaluate the classification performance of Kinect-derived gait data using artificial neural networks (ANNs).

Cross-sectional, exploratory study at a state special education center in eastern Türkiye. Sixty-nine participants aged 18–27 years (ID without DS: n = 46; ID with DS: n = 23; mild–moderate levels) completed three 3-m trials at preferred speed on flat (concrete) and compliant (foam) surfaces. Kinect V1 recorded 3D joint trajectories. Step count, step length, step duration, and walking speed were computed via a threshold-based method. ANN models were trained to classify by DS status, gender, and disability level/age.

On compliant surfaces, step count and walking time increased while walking speed decreased relative to flat surfaces. Performance decrements were more pronounced in participants with DS and those with moderate ID. Women took more steps and walked more slowly than men on both surfaces. ANN models showed high correlation values across tasks (R = 0.96–0.99); however, these findings should be interpreted as exploratory.

A Kinect-based approach may provide a practical and accessible method for characterizing gait in adults with ID with and without DS; however, the findings should be interpreted with caution due to methodological limitations and the need for further validation. Findings support integrating surface-specific balance and gait training, age- and gender-sensitive strategies, and individualized programs for DS. ANN-based analyses may provide preliminary insights for classification purposes; however, their clinical applicability requires further validation and should be interpreted cautiously.

Residual adhesive left on enamel after orthodontic bracket removal can compromise aesthetics and enamel integrity. The Adhesive Remnant Index (ARI) is commonly used to visually assess these residues, but its correlation with true adhesive volume has not been quantitatively validated.

This study aimed to validate the visual ARI using 3D optical scanning and test the hypothesis of no correlation between ARI scores and adhesive remnant volumes.

Seventy-five extracted human third molars were bonded with molar tubes using BrackFix Adhesive. After 24 h, tubes were debonded, and ARI scores (0–3) were assessed visually under 6.0× magnification. Residual adhesive was scanned pre- (T0) and post-debonding (T1) using a structured-light 3D scanner (ATOS Compact). Volumes were calculated and analyzed with Kendall's tau correlation.

A strong correlation was found between ARI scores and adhesive remnant volumes (Kendall's τ = 0.649; p < 0.001). Median volumes increased with ARI scores: 0.048 mm³ (score 0), 0.401 mm³ (score 1), 1.025 mm³ (score 2), and 2.893 mm³ (score 3).

Visual ARI scores significantly reflect the actual adhesive remnant volume as quantified by 3D scanning, supporting ARI's clinical validity for assessing residual adhesive after debonding.

Recent research highlights the pivotal role of gut microbiota and bile acids as modulators of metabolic homeostasis in type 2 diabetes (T2D). The concomitant use of probiotics and ursodeoxycholic acid (UDCA) may potentiate glycemic and lipid control via complementary mechanisms.

To evaluate the metabolic effects of probiotic supplementation and its combination with UDCA in metformin-treated T2D patients.

In this monocentric, prospective, randomized, double-blind, controlled trial, 90 patients with T2D on metformin therapy were randomized into three groups: metformin-only (MG), metformin plus probiotic (MPG), and metformin plus probiotic plus UDCA (MPUG). The intervention lasted 4 weeks. Primary outcomes included changes in fasting glucose, postprandial glucose and HbA1c. Secondary outcomes included lipid profile, C-reactive protein (CRP), and fecal levels of probiotics and UDCA. Two visits were conducted during the study - at the beginning and at the end. Visits involved patient interviews, clinical data collection, anthropometric measurements, blood biochemical analyses, and stool sample analysis for the presence of probiotic culture and UDCA concentrations.

After 4 weeks, the MPUG group showed a significant reduction in fasting glucose (−1.7 mmol/L; 95% CI: −2.2 to −1.2), postprandial glucose (−1.3 mmol/L; 95% CI: −1.8 to −0.7), and HbA1c (−0.49%; 95% CI: −0.66 to −0.31) compared to the MG group. Total cholesterol and LDL cholesterol were also significantly reduced, while HDL increased. The concentration of Lactobacillus rhamnosus GG was highest in the MPUG group. No serious adverse events were reported.

Co-administration of probiotics and UDCA for four weeks in metformin-treated T2D patients significantly improves short-term glycemic control and lipid profiles. These promising results warrant validation in larger, longer-term clinical trials.

In the digital era, iGaming has become a rapidly expanding phenomenon that generates complex behavioral patterns and increases the risk of problematic gambling. The integration of technologies such as Artificial Intelligence of Things and the Internet of Behavior enables a shift from reactive to preventive approaches in public health protection.

This study combines clinical practice, AI, machine learning and the Internet of Behavior concept to analyze player behavior patterns and develop machine learning models for early detection of addiction risk through behavioral markers.

Behavioral data were collected from an online game supplier operating through 52 operators in Republika Srpska, Croatia, Romania, Brazil, Somalia, and Mali. Psychiatric expertise and clinical experience were applied to identify harmful behavioral markers, which served as inputs for training MLP neural networks. Models trained per country classified player behavior into recreational, risky, and problematic categories.

The analysis included 109,418 players across three continents, aggregating 5,135,179,510 online slot game bets. Results revealed significant cross-country variation in risky and problematic gambling, shaped by socio-economic, cultural, and regulatory factors.

The integration of AI-driven behavioral analysis with psychiatric insight provides a robust framework for early risk detection and personalized interventions supporting responsible gaming.

The German congress for orthopedics and trauma surgery is the largest congress of this specialty in Europe. It reflects the latest scientific achievements and thus has a lasting impact on both, clinical practice and research. Understanding what is currently moving the specialist groups therefore helps to understand where orthopedics and trauma surgery is heading.

Contribution titles, topics, authors, and cities were extracted from the German Medical Science database (https://www.egms.de/) and using three distinct network analyses: an author network, a city network, and an entity network. Internal ratings were obtained from the congress committee for further elaboration.

The congress comprised 696 accepted papers, most of which were presented in German (72.41%) and in the form of lectures (57.61%), focusing on topics such as “spine,” “basic research” and “polytrauma.” The city network revealed geographical clusters, with Berlin, Munich and Dresden forming the most closely networked nodes. In the entity network, three major communities emerged, focusing on clinical care, fracture research and joint-related studies. Of the 1232 submissions, 85.80% received a majority favorable review, and 56.49% were accepted, with the acceptance rate increasing in proportion to the review score.

The analysis of the DKOU 2021 revealed a broad scientific focus and research communities centered on clinical care, fracture studies and joint-related research. Transparent evaluation processes and global longitudinal comparisons are recommended to align research with the global burden of disease, taking into account the limitations of congress-based analyses and the influence of external factors such as the Covid-19 pandemic.

The use of social media platforms like YouTube has surged among patients and their families seeking medical information. Despite the widespread use of video content for health education, no previous study has systematically evaluated the quality of YouTube videos on dynamic spinal stabilization.

A YouTube search using the keyword “dynamic stabilization” was conducted in December 2024. Thirty eligible videos were assessed independently by two neurosurgery specialists using the DISCERN scale. Video characteristics and content quality were analyzed using descriptive statistics, correlation analysis, and regression models.

The overall quality of the videos was low, with a mean DISCERN score of 39.15. A negative correlation was found between the number of views and DISCERN score (r = –0.28), while positive correlations were observed between DISCERN scores and video duration (r = 0.19), and between DISCERN-1 and DISCERN-2 scores (r = 0.957). Videos including:

a medically trained speaker,

Most YouTube videos on dynamic stabilization offer low-quality and potentially misleading information. Medically trained contributors consistently produce more reliable and informative content, yet such videos often receive less viewer engagement. Increasing the visibility and clarity of evidence-based content is essential to improve patient education through social media.