Studies in Medical and Health Sciences

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

Alton Mabina, Amber Mbotho — Mon, 27 Jan 2025 00:00:00 +0000

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

Integrating Explainable AI for Skin Lesion Classifications: A Systematic Literature Review

Muhammad Bilal Jan, Muhammad Rashid, Raja Vavekanand, Vijay Singh — Mon, 27 Jan 2025 00:00:00 +0000

Skin cancer, particularly melanoma, poses a significant global health challenge due to its prevalence and mortality rate. Early detection is critical to improving outcomes, as advanced cases become increasingly difficult to treat. The advent of Artificial Intelligence (AI) and Explainable AI (XAI) techniques has revolutionized dermatological diagnostics by offering accurate and interpretable solutions. This systematic review investigates the integration of XAI in skin lesion classification, analyzing 22 recent studies published between 2019 and 2023. The studies encompass diverse approaches, including deep learning models like CNNs, ResNet, DenseNet, and MobileNet, as well as explainability techniques such as Grad-CAM, SHAP, and saliency maps. Results highlight significant advancements in accuracy and interpretability, with some models achieving over 99% accuracy on datasets like ISIC 2018 and HAM10000. However, challenges persist, including dataset imbalances, limited diversity in patient metadata, and generalizability across different skin types and imaging conditions. XAI methods, by visualizing model decision pathways, enhance transparency, fostering trust among clinicians and enabling seamless AI integration into clinical practice. This review underscores the potential of combining state-of-the-art AI models with explainable frameworks to address the complexities of skin lesion diagnostics. It advocates for future research to prioritize diverse, metadata-rich datasets, innovative optimization techniques, and robust architectures to develop reliable, interpretable diagnostic tools. By bridging the gap between advanced AI and user understanding, this work contributes to the creation of clinically applicable, trustable AI-driven healthcare solutions.

Density of Dengue Vector in Hodeidah, Yemen 2017

Sun, 03 Aug 2025 00:00:00 +0000

Background: Dengue fever is a significant vector-borne disease in tropical and subtropical regions, with Aedes aegypti and Aedes albopictus being the primary vectors. Hodeidah, Yemen, has witnessed increasing dengue cases, necessitated an in-depth analysis of vector density. Objective: This report aimed to investigate the density of dengue vector (Aedes aegypti) in Hodeidah in 2017. Methods: The entomological survey was conducted in Hodeidah Governorate, Yemen in 2017. Data were collected across four seasons—winter, spring, summer, and autumn—through field inspections of mosquito breeding sites. Standard indices, including the House Index (HI), Container Index (CI), and Breteau Index (BI), were calculated based on larval and pupal presence. Environmental parameters such as temperature, humidity, and rainfall were also recorded to assess their correlation with vector density. Results: The entomological survey revealed notable differences in the density of Aedes aegypti between urban and rural districts in Hodeidah Governorate. In urban districts, including Al Hali, Al Hawak, and Al Mina, the mean House Index (HI) was 33.3%, the Container Index (CI) was 17.2%, and the Breteau Index (BI) was 57.23. These levels, while concerning, were significantly lower than those observed in rural districts. In contrast, rural areas showed a higher risk of dengue transmission, with a mean HI of 52.3%, CI of 23.4%, and an alarming BI of 139.9. Several districts such as As Salif, Az Zaydiyah, and Al Qanawis reported BI values exceeding the World Health Organization (WHO) epidemic threshold of 50, reaching up to 330.0 in As Salif. Conclusion : The study revealed a seasonally dynamic trend, with peak infestation observed particularly in autumn and spring. These high values suggest intense but uneven breeding activity across rural settings. The variation in vector indices across districts indicates that while urban areas face persistent risk due to population density and poor sanitation, rural districts may act as hotspots for outbreak initiation due to weak surveillance and inadequate vector control. Coordinated public health action, tailored to local epidemiological and environmental conditions, remains essential to curbing dengue transmission.

Epidemiological Features of Dengue Fever in Hodeidah Governorate, Yemen: Outbreak 2019-2020

Tue, 05 Aug 2025 00:00:00 +0000

Background: Dengue fever, a mosquito-borne viral disease caused by the dengue virus (DENV), remains a significant public health challenge, particularly in tropical regions such as Hodeidah, Yemen. The 2019 - 2020 outbreak in Hodeidah highlighted the urgent need to understand its epidemiological features to improve prevention and control strategies. Objective: This study aimed to describe the epidemiological features of dengue fever patients in Hodeidah governorate from 2019 to 2020. Methods: A retrospective study used data from the Center of Tropical Medicine and Infectious Diseases (CTMID) in the Authority of Public at Al Thawara Hospital, Hodeidah, Yemen, that included 3874 dengue cases recorded between November 2019 and March 2020. Blood samples were collected and analyzed for dengue infection, and concurrent conditions such as malaria and thrombocytopenia were also documented. Data were analyzed using Microsoft Excel 2019 and SPSS version 27.0. Results: The results showed that the males were 65% (n = 2529) while the females were 35% (n = 1345). This difference was statistically significant (p-value < 0.00001). In addition, the majority of cases (47%) were observed in the 19–50 years age group, followed by the 6–18 years age group (30%). Children aged 1–5 years accounted for 18 % of the cases, whereas individuals over 50 years represented only 5%. The age distribution of cases reveals a significant variation across (p-value = 0.00001). Also, the distribution of cases across districts shows a significantly higher concentration in urban areas more than rural that indicated statistically significant association between district and case distribution (p-value < 0.00001). On the other hand, dengue infection was predominant, accounting for the majority of cases. Hemorrhagic dengue fever (HDF) was the most frequent diagnosis (45.64%), followed closely by dengue fever (DF) (42.90%). Dengue shock syndrome (DSS) was relatively rare, comprising only 0.46% of the total cases. Co-infections of dengue and malaria were notable, with 5.45 % of cases involving DF-malaria and 5.55 % involving HDF – malaria coinfection. Finally, the attack rate (AR) was reported 0.11 % and the case fatality rate (CFR) was 0.57 %. Conclusion: The study concluded a significant gender disparity among reported cases, with males comprising more than females. The age associated with dengue infection where the adults and children. Dengue was the predominant infection, with hemorrhagic dengue fever (HDF) being the most common diagnosis, followed by dengue fever (DF). Co-infections with malaria were noted. The CFR was less than 1%. Integrated public health measures, including enhanced surveillance, vector control, and community education, are critical for reducing dengue transmission in high-risk areas. These insights can guide policymakers in developing targeted interventions for future outbreaks of the disease.