Healthcare Applications Research Articles

A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research

Selecting the appropriate deep learning (DL) model for healthcare research poses a significant challenge due to the diversity of evaluation criteria and the complex nature of health-related tasks, where a single metric like accuracy is often insufficient. Motivated by the need for a structured, multi-criteria approach, this study proposes a Multi-Criteria Decision Analysis (MCDA) framework using the Analytic Hierarchy Process (AHP). Our primary contribution is the development of a comprehensive decision-making framework that integrates multiple evaluation criteria, such as accuracy, sensitivity, specificity, and computational complexity, alongside empirical data from existing literature to systematically compare DL models. The framework was validated through a use case involving the selection of the best DL model for diagnosing COVID-19 using X-ray images, where we compared eight popular models, including ResNet34, SqueezeNet, and AlexNet, and it was also evaluated through comparative scenarios using traditional methods, including weighted sum, weighted average, and accuracy-based evaluation. Quantitative results show that SqueezeNet achieved the highest score in the AHP framework (88.64), while ResNet34 performed best in traditional methods such as weighted sum (588.49) and accuracy ranking (98.33%). A sensitivity analysis further demonstrated the impact of varying criteria weights, showing how changes in the importance of accuracy and precision, influenced model ranking. These findings highlight the flexibility and robustness of the AHP framework in addressing the complexities of model selection in healthcare research. The implications of this work suggest that a structured, data-driven evaluation approach can provide more nuanced and reliable insights compared to traditional methods like single-metric evaluations, ultimately supporting more informed decision-making in healthcare applications.

A Preliminary Sentiment Analysis on Digital Barriers among Senior Citizens During Covid-19 Pandemic

Nowadays, digital technology has been an important element in assisting the community in their daily routine. During the COVID-19 era, The Special Committee for Ensuring Access to COVID-19 Vaccine Supply (JKJAV) has been initiated to assist the health and well-being of Malaysian citizens in recovering from the COVID-19 pandemic. MySejahtera, a healthcare application technology, has been used by Malaysian citizens for seeking vaccination information as well as updating their health status. While there are benefits to using the application, there is still a challenge in using the technology among senior citizens. This study aims to investigate digital barriers to using a healthcare application, known as MySejahtera, among senior citizens in Kelantan. This study also reveals lessons learned regarding the use of technology among senior citizens during the Covid-19 era. Convergent parallel mixed method research design is initiated to produce a sentiment analysis for identifying digital barriers issues and challenges among senior citizens in Kelantan. 5 respondents aged from 60 to 80 years old were interviewed to obtain their sentiments on using digital technology for vaccination information and Covid-19 news. This study highlights issues namely awareness of knowledge, misconception about digital technology, information quality and technology usage among the senior citizens in Kelantan. This study summarizes that there is a need to reduce digital barriers among senior citizens to ensure digital inclusion for their benefit in health and well-being.

Blockchain for digital healthcare: case ttudies and adoption challenges

Background The healthcare industry is significantly transforming toward digital and smart healthcare. Blockchain, as an emerging distributed collaborative paradigm, offers a promising solution for ensuring trustworthiness and high availability of services in this evolving healthcare. This paper aims to provide a comprehensive survey of blockchain-based applications in smart healthcare. Methods We first present real-world blockchain use cases in smart healthcare and related fields, outlining the motivations for this study. Next, we review cutting-edge blockchain applications in various domains, including health data sharing, public health management, drug supply chains, insurance claims, and the Internet of Medical Things. A detailed analysis of several blockchain-based healthcare data sharing scenarios is included. Results The findings illustrate the diverse applications of blockchain technology in enhancing healthcare systems, along with a detailed examination of challenges related to technical implementation and adoption. Conclusion We discuss the challenges facing blockchain integration in smart healthcare and propose potential solutions to guide future research in this area.

Hippocrates of Kos (460-377 BC): The Founder and Pioneer of Clinical Medicine.

Hippocrates was the first physician in history to establish medicine as a science and to suggest the boundaries of physicians' behavior towards their patients. The Hippocratic Oath is applied in many healthcare systems worldwide as an ethical guide for doctors graduating from medical school. It determines modern medicine's most significant values, such as physicians' specialization to avoid harm and respect patients' privacy. In this study, we present Hippocrates' contributions to clinical medicine and his innovative ideas for the prevention, diagnosis, and treatment of disease. We also analyze his achievements in the development of the main concepts of several medical specialties, such as neurology with hisapproach to the treatment of epilepsy, surgery with his techniques of antisepsis, urology with his theory on stone disease, orthopedics, and acute medicine, as well as their application in modern healthcare. We have conducted a review of the available literature from PubMed and Google Scholar databases.

Design and implementation of a multifunctional desktop robot for computer peripherals

Abstract As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.

Asymmetric Wettability Hydrogel Surfaces for Enduring Electromyographic Monitoring.

Hydrogel electrode interfaces have shown tremendous promise in the acquisition of surface electromyography (EMG) signals. However, the perspiration or moisture environments will trigger the deadhesion between hydrogel electrodes and human skin. Despite the hydrophobic/hydrophilic surfaces can perform the anti-moisture or adhesion respectively, it remains a challenge to integrally form a Janus hydrogel with homogeneous mechanical elasticity and electronic performance. Herein, a surface induction strategy is proposed to approach the hydrophobic/hydrophilic hydrogel surfaces. The hydrophobic interaction between surfactants and molds regulates the distribution of hydrophobic/hydrophilic monomers on the surface. The hydrophobic molds induce a hydrophilic hydrogel surface, while the hydrophilic molds induce a hydrophobic surface. It presents a new phenomenon of reversal wettability inducing and optional hydrogel surfaces. The integral Janus hydrogel can be easily obtained by the hydrophilic molds. Balance of adhesion, elasticity, and conductivity endows the hydrogel electrode patch with durable conformal adhesion and high-fidelity EMG signals even in the sweaty epidermis due to the asymmetric wettability surfaces. This hydrogel performs the quantitative description of muscle strength and accurate fatigue assessment. It offers a reliable candidate for future practical applications in continuous digital healthcare and intelligent human-machine interaction, even the Metaverse.

Topical formulations contained an extract from marine alga Cladophora glomerata

The Cladophora glomerata is a green alga abundant in fresh and seawater. The extraction of C. glomerata using glycol-based solvents, including butylene glycol (BG), polyethylene glycol 400 (PEG 400), and propylene glycol (PG), as the green solvents to obtain antioxidants and nutrients that can be further used in health care and pharmaceutical applications. The extraction of C. glomerata with glycol-base solvents using the microwave-assisted extraction (MAE) method was optimized. The liquid extraction using 60 % volume by volume of polyethylene glycol yielded the highest total phenolic content (4.00 ± 0.08 mg gallic acid equivalence per g dry weight) and antioxidant activity (IC50 0.14 ± 0.02 mg/mL). The algal extract was stable at pH 6 to 8 and a temperature lower than 25 °C. After incorporating the algal extract in topical formulations including serum and emulgel, we found that the preparations were stable and retained the extract's antioxidant activity. In addition, all algal extracts, extract-loaded serum, and emulgel did not show cytotoxicity effects in normal adult human dermal fibroblasts.

Specification, validation and verification of social, legal, ethical, empathetic and cultural requirements for autonomous agents

Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously defined, checked for consistency, and used to verify the agent. In this paper, we introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents. Our framework comprises: (i) a language for specifying SLEEC rules and rule defeaters (that is, circumstances in which a rule does not apply or an alternative form of the rule is required); (ii) a formal semantics (defined in the process algebra tock-CSP) for the language; and (iii) methods for detecting conflicts and redundancy within a set of rules, and for verifying the compliance of an autonomous agent with such rules. We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot.

Exploring Long-Term Determinants and Attitudes Toward Smartphone-Based Commercial Health Care Applications Among Patients With Cancer.

Our study explores how attitudes of patients with cancer toward smartphone-based commercial health care apps affect their use and identifies the influencing factors. Of the 960 patients with cancer who participated in a randomized controlled trial for a smartphone-based commercial health care app, only 264 participants, who completed a survey on app usage experiences conducted between May and August 2022, were included in this study. Participants were categorized into three groups: Positive Persistence (PP), Negative Nonpersistence (NN), and Neutral (NE) on the basis of their attitude and willingness to use smartphone-based commercial health care apps. The Health-Related Quality of Life (QOL) Instrument (8 Items), European QOL (5 Dimensions; 5 Levels), The Human Interaction and Motivation questionnaire, and open-ended questionnaires were used to examine factors potentially influencing extended utilization of digital interventions. Despite demographic similarities among the three groups, only the PP and NE groups showed similar app usage compared with the NN group. The combined group (positive persistence and neutral) exhibited significant improvement in depression (P = .02), anxiety (P = .03), and visual analog scale scores (P = .02) compared with the NN group. In addition, patient interaction (P < .01) and the presence of a chatbot/information feature on the app (P < .01) demonstrated a significant difference across the three groups, with the most favorable response observed among the PP group. Patients were primarily motivated to use the app owing to its health management functions, while the personal challenges they encountered during app usage acted as deterrents. These findings suggest that maintaining a non-negative attitude toward smartphone-based commercial health care apps could lead to an improvement in psychological distress. In addition, the social aspect of apps could contribute to extending patient's utilization of digital interventions.

A traceable and revocable decentralized attribute-based encryption scheme with fully hidden access policy for cloud-based smart healthcare

Smart healthcare is an emerging technology for enabling interaction between patients and medical personnel, medical institutions, and medical devices utilizing advanced Internet of Things (IoT) technologies. It has attracted significant attention from researchers because of the convenience of storing and sharing electronic medical records (EMRs) in the cloud. Given that a patient’s EMR contains sensitive individual information, it must be encrypted before uploading it to the cloud. As a countermeasure for data confidentiality and fine-grained access control, the Ciphertext Policy Attribute-Based Encryption (CP-ABE) technique is proposed, which helps manipulate private personal data without explicit authorization. However, most CP-ABE schemes use a centralized mechanism which may lead to performance bottlenecks and single-point-of-failure issues. They will also be at risk of key abuse and privacy breaches in smart healthcare applications. To this end, in this paper, we investigate a traceable and revocable decentralized attribute-based encryption scheme with a fully hidden access policy (TR-HP-DABE). Firstly, to overcome the issues of user privacy leakage and single-point-of-failure, a fully hidden access policy is established for multiple attribute authorities. Secondly, to prevent key abuse, the proposed TR-HP-DABE can achieve the tracking and revocation of malicious users by using Key Encryption Key (KEK) trees and updating the partial ciphertext. Furthermore, the online/offline encryption and verifiable outsourced decryption are applied to improve its efficiency in practical smart healthcare. According to our analysis, the security and traceability of TR-HP-DABE can be proved. Finally, the performance evaluation of TR-HP-DABE is more effective than some existing typical ones.

Enhanced air filtration and ammonia sensing with cellulose nanofibers-based triboelectric nanogenerators under harsh environments

Fire accidents often occur in environments characterized by severe air pollution, toxic gas emissions, and microbial growth, creating significant challenges for developing multifunctional portable healthcare devices. These devices must achieve high filtration efficiency, minimal pressure drop, and the capability to provide early warnings of toxic gas leaks. Triboelectric nanogenerators present a sustainable solution as self-powered energy converters for advanced healthcare applications. In this study, we grafted Ti3C2Tx/MoS2 nanohybrid materials, which form Schottky heterojunctions, onto cellulose diacetate using tetraethyl orthosilicate, followed by electrospinning to produce nanofibrous films. When paired with a negatively charged material, the resulting device achieved an optimal balance with a low-pressure drop (52 Pa) and high filtration efficiency (98.72 % for PM0.3). It also demonstrated exceptional stability under high-temperature and high-humidity conditions. Notably, the device maintained rapid sterilization within 15 min and consistent filtration performance even after multiple washes. Furthermore, the device exhibited precise detection of trace amounts of NH3 (0.1 ppm) and demonstrated a rapid response, achieving an 86 % response rate within 2 s for 100 ppm NH3, providing an early warning 28 s faster than commercial NH₃ monitors. This study introduces a novel approach to the development of multifunctional, self-powered, wearable medical devices that offer high-efficiency air filtration and sterilization, breath monitoring, and rapid toxic gas leakage detection in harsh environments.

ZIF-8-enhanced multifunctional, high-performance nanocomposite hydrogel–based wearable strain sensor for healthcare applications

ZIF-8-enhanced multifunctional, high-performance nanocomposite hydrogel–based wearable strain sensor for healthcare applications

Exploring the Landscape of Polymerase Chain Reaction in Indian Medical Research: A Scientometric Analysis

Background and Aims: The polymerase chain reaction (PCR) has revolutionised medical research in India by offering a rapid and precise method for amplifying deoxyribonucleic acid (DNA) sequences crucial for various applications in healthcare. It aims to investigate publication growth, leading authors, productive journals, collaborating countries, keywords, institutional affiliations and funding agencies involved in PCR-related research within India. Methodology: A scientometric analysis assessed India’s PCR research performance using 10,509 articles from Scopus (2013-2022). Biblioshiny and VOSviewer aided data analysis, while Microsoft Excel facilitated computations. Results: The finding reveals the continuous growth in PCR publications, with significant contributions from researchers like Aman Sharma from the Post Graduate Institute of Medical Education and Research (Chandigarh) and journals like the Indian Journal of Medical Microbiology. Notable contributors hail from the All India Institute of Medical Sciences (AIIMS), New Delhi. International collaborations, especially with the United States, underscore the global outreach of PCR research in India. Keyword analysis reveals emerging trends and research themes in PCR, reflecting its evolving role in addressing contemporary health challenges. Funding agencies such as the Indian Council of Medical Research and the Ministry of Science and Technology, India (DBT) are crucial in supporting PCR research endeavours. Conclusions: This study provides valuable insights into the dynamics of PCR research in India, highlighting areas for future investigation and collaboration to further advance medical research and healthcare outcomes.

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

CRISPR-Cas Innovative Strategies for Combating Viral Infections and Enhancing Diagnostic Technologies

Background: CRISPR-Cas technology has transformed molecular diagnostics and therapeutic strategies for viral infections, particularly COVID-19. Its ability to precisely edit viral genomes and detect viral RNA/DNA offers a novel approach to combating persistent viral infections.Objective: This study aimed to evaluate the diagnostic accuracy and therapeutic potential of CRISPR-Cas systems in viral infections, with a focus on COVID-19.Methods: A systematic review and meta-analysis of 25 peer-reviewed studies were conducted, including clinical trials and experimental research. Data collection involved searching PubMed, Scopus, and Google Scholar using keywords such as "CRISPR-Cas," "viral diagnostics," and "COVID-19." Statistical analysis was performed using SPSS version 25, with pooled sensitivity and specificity estimates calculated for CRISPR-based diagnostics.Results: CRISPR-based diagnostics, including SHERLOCK and DETECTR, achieved pooled sensitivity of 94% (95% CI: 92%-96%) and specificity of 97% (95% CI: 95%-99%) for SARS-CoV-2 detection. Therapeutic interventions using CRISPR-Cas9 showed an 84% reduction in viral replication across HIV and Hepatitis B studies (95% CI: 80%-88%).Conclusion: CRISPR-Cas technologies demonstrate high diagnostic accuracy and therapeutic potential, particularly in resource-limited settings. Further clinical validation is needed to enhance global healthcare applications.

Piezoelectric nanogenerators from sustainable biowaste source: Power harvesting and respiratory monitoring with electrospun crab shell powder-poly(vinylidene fluoride) composite nanofibers

Wearable piezoelectric nanogenerators (PENGs) are increasingly significant in healthcare and energy harvesting applications due to their ability to convert mechanical energy into electrical signals. In this study, we developed PENGs by incorporating crab shell powder (CS-NFs) into electrospun polyvinylidene fluoride (PVDF) nanofibers to enhance their piezoelectric properties. The PVDF-CS-NFs (PC-NFs) composites were evaluated for structural, thermal, and piezoelectric performance. The 1.5 wt% CS-NFs composite exhibited a notable improvement, with a maximum output voltage of 19 V under mechanical deformation, significantly higher than pristine PVDF NFs. Furthermore, the device demonstrated excellent sensitivity in real-time respiratory monitoring when applied to various body locations, including the chest, throat, and mask. Additionally, the PC-NFs-based PENGs were capable of charging a 2.2 µF capacitor to 2 V within 180 s and powering 56 LEDs. These results underscore the potential of using sustainable crab shell waste in biocompatible, eco-friendly piezoelectric devices for wearable sensors and energy harvesting applications.

Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS

The current review conducts a comprehensive comparison of Quantum GIS (QGIS) and ArcGIS in the context of healthcare applications, aiming to assist users in selecting the most appropriate GIS platform for their specific needs. Geographic Information Systems (GIS) are essential tools in healthcare, providing capabilities for spatial analysis, disease mapping, and resource allocation. Driven by the need to improve healthcare delivery and public health strategies, this study compares the functionalities, costs, and support systems of QGIS and ArcGIS. The major objective is to determine which GIS platform offers the best balance of cost-efficiency and performance across various healthcare scenarios. The methodology encompasses a detailed comparative analysis, incorporating quantitative data from user satisfaction surveys, performance evaluations, and real-world case studies. Findings reveal that QGIS, as an open-source GIS software, is highly cost-effective and customizable, making it particularly advantageous for budget-limited projects and smaller organizations requiring flexibility. Its extensibility via plugins and robust user community fosters continuous innovation and improvement. QGIS proves exceptionally effective in healthcare applications such as disease mapping and accessibility analysis, where customization and cost-efficiency are critical. Conversely, ArcGIS is distinguished by its advanced analytical capabilities and strong performance in handling complex, large-scale studies. Its sophisticated tools and comprehensive spatial analysis functionalities are essential for thorough healthcare research and planning. Although ArcGIS incurs higher costs, its extensive feature set, professional support, and exhaustive documentation justify the investment for large organizations and research institutions engaged in detailed GIS projects. In conclusion, both QGIS and ArcGIS exhibit significant strengths in key healthcare applications. QGIS excels in affordability and flexibility, while ArcGIS provides superior analytical power and support for extensive projects. Future research should focus on integrating real-time data and enhancing user experience to further optimize GIS applications in healthcare.

A SUPERVISED ONLINE CROWD ANALYSIS NETWORK WITH DUAL-TASK DEEP LEARNING IDEA IN HEALTHCARE APPLICATION

A SUPERVISED ONLINE CROWD ANALYSIS NETWORK WITH DUAL-TASK DEEP LEARNING IDEA IN HEALTHCARE APPLICATION

Enhancement of broadband terahertz trace absorption spectrum based on angular multiplexing of trapezoidal dielectric grating

Terahertz spectroscopy provide a fast, label-free, and non-destructive method for detecting bio-medical molecules, with wide applications in national security, pharmacy, and healthcare. However, detecting terahertz absorption spectra of trace analytes still faces significant challenges. In recent years, metamaterial parameter multiplexing technology has been used to detect the terahertz fingerprint spectra of trace substances. This paper proposes a new structure based on the angle-multiplexing enhancement principle of a trapezoidal dielectric grating to enhance the terahertz absorption spectra of thin-film analytes. By scanning the incident wave angle, a series of resonance absorption peaks can be obtained. The enhanced absorption spectrum formed by the envelope of absorption spectrum peaks increases by 141 times compared to directly measuring the absorption spectrum of the same film. This structure provides a new and effective means for terahertz trace detection using metamaterial parameter multiplexing technology.