Innovations in quantitative rapid testing: Early prediction of health risks.

The rapid evolution of smart wearable sensors has revolutionized the landscape of continuous health monitoring, enabling a paradigm shift from reactive to proactive healthcare delivery. These advanced devices integrate a range of miniaturized, non-invasive sensors capable of capturing real time physiological and environmental data, offering critical insights into an individual's health status. With the convergence of wireless communication, cloud computing, and artificial intelligence, wearable technologies are now equipped to process complex data streams and deliver actionable health feedback to both users and healthcare providers. This chapter presents a comprehensive exploration of the technological foundations, sensor types, emerging innovations, and key applications of smart wearable devices in healthcare. It highlights how machine learning and AI are enhancing data interpretation and personalization, while also addressing significant challenges such as data interoperability, system integration, and security compliance. The chapter identifies existing research gaps and emerging trends that are shaping the future direction of wearable health monitoring systems. By synthesizing current advancements and unresolved issues, this work aims to support future research and development efforts in creating more intelligent, accessible, and effective wearable health solutions.

Background The rapid evolution of health monitoring technologies has transformed the landscape of chronic and preventive healthcare, with applications spanning diabetes, cardiovascular, and general health management. Continuous glucose monitoring systems, wearable electrocardiography (ECG), and mobile health applications provide real-time, accessible data to support early diagnosis, personalized treatment, and proactive patient engagement. This review aims to systematically examine current health monitoring technologies, their diagnostic accuracy, patient outcomes, and implementation challenges. Methods A systematic literature search of 55 peer-reviewed articles was conducted, focusing on diabetes and cardiovascular monitoring devices, general health trackers, and methodological approaches in health monitoring. Results Findings indicate that diabetes monitoring technologies, such as continuous glucose monitors, significantly improve glycemic control and reduce emergency hospitalizations. Cardiovascular health monitoring technologies, including wearable ECG and blood pressure devices, demonstrate potential in early detection and continuous risk assessment. General health monitoring tools, particularly fitness and wellness applications, show effectiveness in promoting preventive behaviors, though long-term efficacy remains uncertain. Common challenges identified include data security, integration into healthcare systems, and reliability across diverse populations. Conclusion This review underscores the strengths and limitations of these technologies and highlights the need for robust data management strategies and evidence-based integration into clinical practices. Future research should focus on enhancing interoperability, addressing privacy concerns, and evaluating outcomes across diverse populations to fully harness the potential of health monitoring technologies in clinical and community health settings.

An essential part of the hydraulic engineering systems, hydraulic gates regulate water flow in dams and other structures. To avoid structural problems, they must ensured to be consistent. Multi-sensor data fusion has become a key tool for health monitoring with advances in machine learning (ML) and sensor technologies. This study aims to provide a novel multi-sensor data fusion health monitoring system for hydraulic engineering gates. To provide more effective and precise real-time monitoring of hydraulic engineering gates, a unique Dynamic Satin Bowerbird search-driven Bidirectional Long Short-Term Memory (DSB-BiLSTM) model were proposed. The hydraulic monitoring dataset was collected from Kaggle website, comprising information and some attributes obtained from hydraulic engineering gates. To improve data quality, noise reduction, and data normalization are used. Time-series analysis techniques extract important information such as vibration patterns and pressure variations. The DSB-BiLSTM model leverages bidirectional LSTM to progress time-dependent data, and the Satin Bowerbird search optimizes sensor selection. The method shows better accuracy and efficiency in identifying potential faults compared to traditional models. When compared to four attributes such as pump, cooling, valve and accumulator with the metrics outcomes of 0.9901, 0.9925, 0.9950, and 0.9890 in accuracy, 0.9930, 0.9960, 0.9902, and 0.9889 in precision, 0.9920, 0.9960, 0.9911, and 0.9890 in recall, and 0.9915, 0.9955, 0.9905, and 0.9889 in F1-score, the DSB-BiLSTM model performs better than all other approaches that were examined. This suggests better overall hydraulic engineering gate health monitoring performance. The proposed DSB-BiLSTM model is a show potential solution for real-time health monitoring of hydraulic gates, provided that it enhances accuracy and reduces computational complexity.

BACKGROUND Wearable sensor technologies, such as inertial measurement units (IMUs), smartwatches, and multi-sensor systems, have emerged as valuable tools in clinical and real-world health monitoring. These devices allow continuous, non-invasive tracking of gait, mobility, and functional health across a variety of populations. However, significant challenges remain, including variability in sensor placement, data processing methodologies, and insufficient validation in real-world settings. OBJECTIVE This systematic review aims to evaluate recent literature on the clinical and research applications of wearable sensors. Specifically, it investigates how these technologies are used to assess mobility, predict disease risk, and support rehabilitation. It also identifies limitations and proposes future research directions. METHODS The review was conducted according to PRISMA guidelines. A comprehensive search of PubMed, Scopus, and Web of Science databases was performed for studies published in the past ten years. Inclusion criteria focused on studies using wearable sensors in clinical or real-world environments. A total of 30 eligible studies were identified for qualitative synthesis. Data extracted included study design, population characteristics, sensor type and placement, machine learning algorithms, and clinical outcomes. RESULTS Among the reviewed studies, observational designs were the most common (43.3%), followed by experimental studies (26.7%) and randomized controlled trials (10%). IMU-based sensors were used in 66.7% of studies, with wrist-worn devices being the most common placement (43.3%). Machine learning techniques were frequently applied, with random forest (20%) and deep learning (16.7%) models predominating. Clinical applications spanned Parkinson’s disease, stroke, multiple sclerosis, and frailty, with several studies reporting high predictive accuracy for fall risk and mobility decline (AUROC up to 0.919, p < 0.05). CONCLUSIONS Wearable sensors demonstrate strong potential for enhancing mobility monitoring, disease risk assessment, and rehabilitation tracking in both clinical and real-world settings. However, challenges remain in standardizing sensor protocols and data analysis. Future research should focus on large-scale, longitudinal studies, harmonized machine learning pipelines, and integration with cloud-based health systems to improve scalability and clinical translation.

The rapid advancement of artificial intelligence (AI)-driven diagnostics and wearable health technologies is transforming health care delivery by enabling real-time health monitoring and early disease detection. These innovations are catalyzing a shift toward personalized medicine, with interventions tailored to individual patient profiles with unprecedented precision. This paper examines the current National Institute for Health and Care Excellence (NICE) evidence standards framework (ESF) for digital health technologies (DHTs) and evaluates the challenges associated with integrating DHTs into existing health and care systems. A comprehensive review of the NICE ESF guidelines was conducted, alongside an evaluation of their applicability to emerging AI and wearable technologies. Key limitations and barriers were identified, with particular focus on the framework's responsiveness to technologies that evolve through machine learning and real-world data integration. Our findings indicate that while the NICE ESF provides a structured approach for evaluating DHTs, it lacks the adaptability required for rapidly evolving innovations. The framework does not sufficiently incorporate real-world evidence or support continuous learning models, which are critical for the safe and effective deployment of AI-based diagnostics and wearables. To remain effective and relevant, the NICE ESF should transition to a dynamic, adaptive model co-designed with industry stakeholders. By embedding real-world evidence-based strategies and promoting transparency, efficiency, and collaborative innovation, the updated framework would better facilitate the integration of AI-driven diagnostics and wearables into health care systems, ultimately enhancing patient outcomes and optimizing health care delivery.

<p>Machine learning (ML) provides a promising paradigm for discovering and modeling structural performances and conditions through the deep mining of structural health monitoring data. This paper exhibits recent progress of ML-based structural health monitoring and condition assessment for long-span bridges. A series of novel algorithms for bridge condition assessment via correlation modeling between structural responses, computer vision-assisted structural damage detection, and data mining for wind effects are introduced. First, correlation modeling between different bridge responses is investigated by both the time series data and probability distribution, further assisting bridge condition assessment. Second, several novel CNN architectures and a few-shot meta-learning framework are also established for CV-assisted bridge damage detection. Third, wind-induced vibrations of the bridge in site are identified and modeled to predicate structural responses and evaluate operation conditions. Results show that ML techniques indeed improve the state of the art in structural health monitoring and condition assessment for long-span bridges.</p>

Artificial intelligence (AI) is transforming healthcare by improving diagnostic accuracy and predictive analytics. Periodontal diseases are recognized as risk factors for systemic conditions, including type 2 diabetes mellitus, cardiovascular disease, Alzheimer's disease, polycystic ovary syndrome, thyroid dysfunction, and post-coronavirus disease 2019 complications. These conditions exhibit complex bidirectional interactions, underscoring the importance of early detection and risk stratification. Current diagnostic tools often fail to capture these interactions at an early stage, limiting timely intervention. This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions, enhancing clinical outcomes. To evaluate AI's role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024. This systematic review followed PRISMA guidelines (2009) and included peer-reviewed articles from PubMed, Scopus, and Embase. Studies with large sample sizes (≥ 500 participants) were selected, focusing on AI models integrating multi-omics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging. Machine learning models processed structured clinical data, deep learning models combined imaging and clinical data, and natural language processing models extracted insights from clinical notes. AI applications significantly enhanced diagnostic and predictive accuracy, reducing diagnostic time by 40% and improving predictive accuracy by 25% in periodontal patients with type 2 diabetes mellitus. Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%, with specificity and sensitivity rates of 94% and 90%, respectively. Increasing sample sizes over the years reflected advancements in AI, data collection, and model training, reinforcing model reliability. AI's integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions, improving clinical outcomes and decision-making.

Smart healthcare has increased to meet the needs of the growing human population and medical expenses. People are all hurrying to catch up with work schedules, academic appointments, and social engagements, especially in this jet age. These often happen at the detriment of our health. Healthcare services, especially those that provide optimal healthcare delivery, face many problems, such as the ineffective provision ofhealth monitoring applications andless emphasis on disease prediction systems.Severalresearchstudies have been carried out in an attempt to proffer solutions to the peculiar problems; however, the problem persists. Therefore, this paper develops a Cardiovascular disease prediction system with specific objectives: implement data analysis for disease prediction using a k-Nearest Neighbors (k-NN)-based machine learning system; evaluate the performance of the developed cardiovascular disease prediction system with existing health monitoring systems. The k-Nearest Neighbors was utilized using a 1025 dataset and 18 attributes collected from the UCI machine learning repository. The results show that k-NN achieved an accuracy of 99.21%. k-Nearest Neighbors algorithm is a non-parametric machine learning that majority voting to classify new case of cardiovascular disease, and non-sensitive to noise and outlier. The proposed model is higher than the existing system, which shows an average accuracy result of 84.63%. The developed machine learning approach will guide healthcare practitioners on the use of machine learning for cardiovascular disease diagnosis and prediction.

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

Triboelectric nanogenerators (TENGs) have become a vital technology in physical health monitoring and rehabilitation applications, enabling continuous, personalized, and convenient monitoring, facilitating early detection and prevention, and offering valuable data‐driven insights. On‐body wearable triboelectric sensors enable real‐time tracking of vital health parameters, promoting accurate and personalized healthcare interventions. In addition to being self‐powered, these sensors facilitate early detection and prevention by capturing early warning signs of potential health issues, leading to timely interventions. Furthermore, they play a vital role in rehabilitation monitoring and feedback, helping to assess progress during the recovery process. This continuous monitoring, along with artificial intelligence, provides data‐driven insights into patterns, trends, and correlations, facilitating evidence‐based healthcare decision‐making. In this review, we provide an overview of the recent wearable TENG developments for healthcare applications. We discuss how TENGs work and explore a wide range of self‐powered health monitoring and rehabilitation applications. We then summarize recent advancements in healthcare applications, material selection, and unique features to help design highly efficient, accurate, and practical bioapplicable technology. Further, we discuss the challenges and prospects of the TENGs technology for healthcare applications, including miniaturization, moisture resistance, device maintenance, data reliability, and high outpower requirements.

The changing landscape of concrete bridge infrastructure health monitoring and informed maintenance strategies: A decade of developments and trends

The assessment of the health status and prediction of the lifespan of cable equipment are critical for ensuring the stability and efficiency of the power grid. This paper develops a temperature-current-capacity-life calculation model for cables, considering the fast and slow charging demands of electric vehicles (EVs). Analyses under scenarios of rapid and slow charging demands are conducted, introducing a cable health index and establishing a health status assessment framework based on this index. The framework accounts for various factors leading to cable faults, offering a comprehensive evaluation of the health status of cables with different fault rates. Building upon this, a prediction method using the Fire Hawk Optimization (FHO) Algorithm and Convolutional Neural Network (CNN) is proposed. This method enhances performance by optimizing the hyperparameters of Bidirectional Gated Recurrent Unit (BiGRU) through FHO, effectively searching and determining the optimal hyperparameter configuration. The impact of different scenarios and varying EV penetration rates on cable temperature is analyzed through case studies, facilitating the assessment and prediction of health status.

Background/Objectives: Acute respiratory distress syndrome (ARDS) represents a critical complication in polytrauma patients, characterized by diffuse lung inflammation and bilateral pulmonary infiltrates with mortality rates reaching 45% in intensive care units (ICU). The heterogeneous nature of ARDS and complex clinical presentation in severely injured patients poses substantial diagnostic challenges, necessitating early prediction tools to guide timely interventions. Machine learning (ML) algorithms have emerged as promising approaches for clinical decision support, demonstrating superior performance compared to traditional scoring systems in capturing complex patterns within high-dimensional medical data. Based on the identified research gaps in early ARDS prediction for polytrauma populations, our study aimed to: (i) develop a balanced random forest (BRF) ML model for early ARDS prediction in critically ill polytrauma patients, (ii) identify the most predictive clinical features using ANOVA-based feature selection, and (iii) evaluate model performance using comprehensive metrics addressing class imbalance challenges. Methods: This retrospective cohort study analyzed 407 polytrauma patients admitted to the ICU of the Center of Traumatology and Major Burns of Ben Arous, Tunisia, between 2017 and 2021. We implemented a comprehensive ML pipeline that incorporates Tomek Links undersampling, ANOVA F-test feature selection for the top 10 predictive variables, and SMOTE oversampling with a conservative sampling rate of 0.3. The BRF classifier was trained with class weighting and evaluated using stratified 5-fold cross-validation. Performance metrics included AUROC, PR-AUC, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Results: Among 407 patients, 43 developed ARDS according to the Berlin definition, representing a 10.57% incidence. The BRF model demonstrated exceptional predictive performance with an AUROC of 0.98, a sensitivity of 0.91, a specificity of 0.80, an F1-score of 0.84, and an MCC of 0.70. Precision-recall AUC reached 0.86, demonstrating robust performance despite class imbalance. During stratified cross-validation, AUROC values ranged from 0.93 to 0.99 across folds, indicating consistent model stability. The top 10 selected features included procalcitonin, PaO2 at ICU admission, 24-h pH, massive transfusion, total fluid resuscitation, presence of pneumothorax, alveolar hemorrhage, pulmonary contusion, hemothorax, and flail chest injury. Conclusions: Our BRF model provides a robust, clinically applicable tool for early prediction of ARDS in polytrauma patients using readily available clinical parameters. The comprehensive two-step resampling approach, combined with ANOVA-based feature selection, successfully addressed class imbalance while maintaining high predictive accuracy. These findings support integrating ML approaches into critical care decision-making to improve patient outcomes and resource allocation. External validation in diverse populations remains essential for confirming generalizability and clinical implementation.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

PurposeFrom on-board automotive diagnostics to real-time aircraft state of health, the implementation of health monitoring and management systems are an increasing trend. Further, reductions in operating budgets are forcing many companies and militaries to consider new operating and support environments. Combined with longer service lives for aircraft and other systems, maintenance and operations processes must be reconsidered. The majority of research efforts focus on health monitoring techniques and technologies, leaving others to determine the maintenance and logistics impact on the systems. The paper aims to discuss these issues.Design/methodology/approachThis research analyzes the impact of a health monitoring system on a squadron of aircraft. Flight, maintenance and logistics operations are stochastically modeled to determine the impact of program decisions on supply metrics. An arena discrete event simulation is utilized to conduct this research on 20 components on each of the 12 aircraft modeled. Costs and availability are recorded for comparison across three sparing scenarios to include economic order quantity (EOQ) for baseline and health monitoring cases and a just-in-time (JIT) health monitoring set of simulations.FindingsData are presented for EOQ and JIT supply methods. A comparison of health monitoring enabled supply to current methods shows cost savings and availability gains. The different methodologies are compared and discussed as a trade-space for programmatic decisions.Originality/valueThis work demonstrates the ability of health monitoring systems and condition based maintenance to affect supply ordering decisions. The development of trade-spaces within operating environments is demonstrated along with the ability to conduct cost benefit analyses.