Shruthi S^*, D. Hanumanth Rao Naidu

Structural and Congenital Heart Disease, Vol.21, No.2, 2026, DOI:10.32604/schd.2026.079590 - 11 June 2026

Abstract Background: Congenital Heart Disease (CHD) is an abnormality of the heart arising before birth. CHD diagnosis poses a critical challenge, particularly in resource-constrained settings where access to doctors and skilled radiologists is limited. The maternal risk factors contributing to CHD include modifiable and non-modifiable causes. Very few studies mention about these maternal risk factors for the Indian population to build predictive machine learning models for disease forecasting. The aim is to explore the feasibility of predicting CHD occurrence using maternal risk factor data and machine learning models in an Indian context. Methods: This research utilizes Indian-origin… More >

Shotaro Nozaki, Hiroyuki Oikawa, Naofumi F. Sumitomo^*, Kentaro Tomita, Satoshi Narumi

Structural and Congenital Heart Disease, Vol.21, No.2, 2026, DOI:10.32604/schd.2026.076771 - 11 June 2026

Abstract Background: Many anesthetics suppress atrioventricular conduction and may exacerbate atrioventricular block (AVB), which has led to the establishment of pediatric perioperative guidelines. However, the perioperative management of patients with a history of AVB who have recovered to an apparent sinus rhythm remains unclear. Case presentation: We report the case of a 13-year-old girl who developed complete AVB following surgery for congenital heart disease in infancy and subsequently recovered sinus rhythm. She experienced a recurrence of AVB after anesthesia induction for scoliosis surgery. An implantable pacemaker was inserted in infancy after the onset of complete AVB.… More >

Kiran Deep Singh¹, Prabh Deep Singh², Narinder Kaur³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080915 - 27 May 2026

Abstract In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images. This study introduces an image acquisition technique that leverages edge… More >

Ronak Patel¹, Miral Patel², Deep Kothadiya³, Noor A. Khan⁴, Shaha Al-Otaibi^5,*, Roaa Khalil Mohamed Ali Abed⁶, Tanzila Saba⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080819 - 27 May 2026

Abstract Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module… More >

Fahad Algarni^1,2,*, Saeed Ullah Jan³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077157 - 08 May 2026

Abstract With the increasing connectivity and intelligence of Internet-of-Things (IoT) devices, which interface with numerous aspects of our daily lives, security remains a major concern for IoT devices deployed in e-healthcare systems. The existing solutions demonstrate that authentication of IoT devices across all domains, especially in healthcare, poses significant vulnerabilities, including side-channel, insider, and replay attacks. Alternatively, it is not feasible for resource-constrained IoT devices due to the computational, communicational, and space overheads of modular exponentiation or bilinear pairing, or because it requires four to five round-trips for authentication. The rapid growth of IoT in the… More >

Urvashi Chaudhary^1,2,*, Samikkannu Rajkumar³, Dushantha Nalin K. Jayakody^1,2,4, Yakubu Tsado⁵, Bamidele Adebisi⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.070014 - 08 May 2026

Abstract This research paper explores how wireless communication has played a key role in transforming healthcare and bringing in a new era of personalized, linked, and data-driven medical services. With the proliferation of wireless healthcare applications, ensuring the security and privacy of sensitive medical data has become paramount. We discuss the potential of blockchain technology, to address challenges and to secure next-generation wireless healthcare. We have elaborated how blockchain’s core characteristics, such as immutability and decentralization, can create a secure and transparent environment for sharing and storing medical data. Additionally, this paper examines how emerging technologies,… More >

Nomangwane Angelina Tshabalala¹, Ping Guo^2,*

Journal on Internet of Things, Vol.8, pp. 87-107, 2026, DOI:10.32604/jiot.2026.078248 - 06 May 2026

Abstract Type 2 Diabetes mellitus is a disease that afflicts approximately 537 million individuals all over the world, and continuous glucose monitoring (CGM) systems have become very important in the management of the disease. Nonetheless, the existing centralized data architecture of CGM generates high privacy and security risks, as sensitive patient health data can be easily abused. This paper introduces an original structure that incorporates both federated learning and blockchain technology and allows for predicting glucose safely and preserving privacy without affecting the integrity of the data. Our model uses the Long Short-Term Memory (LSTM) neural… More >

Maram Fahaad Almufareh^1,*, Mamoona Humayun², Sadia Din^3,*, Khalid Haseeb⁴, Amr Munshi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080287 - 27 April 2026

Abstract The real-time systems perform key functionalities in various fields to automate the communication and response in critical events. The Internet of Things (IoT), integrated with numerous physical objects, gathers environmental data, processes it at the edge, and provides intelligent decisions while routing health records to processing units. However, the dynamic and resource-constrained nature of IoT-based healthcare environments introduces significant challenges related to latency, transmission costs, and the reliable interaction of devices amid uncertain activities. In this work, we propose a framework for a consistent and trustworthy system that uses a weighted trust aggregation model to More >

Muhammad Waqar¹, Zeshan Aslam Khan^1,*, Arthur Chang^2,*, Zhishan Guo³, Chun-Liang Lai⁴, Chuan-Yu Chang⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078892 - 27 April 2026

Abstract Lung cancer (LC) is among the dangerous cancers spreading progressively, and a timely LC diagnosis becomes a dire need of the time. Various imaging-based studies have been conducted for accurate LC examination through computed tomography (CT), X-ray, and histopathology. Worldwide, the proportion of LC-affected patients in hospitals is growing, thereby increasing imaging data for fast processing and early examination. To facilitate histopathological imaging-based automated and timely decision making for accurate LC prediction, a Context Aware Fusion Network (CAFNet) for holistic feature learning and spatially localized feature learning is proposed in this study for the efficient… More >

Mark W. Shilling¹, Shawn L. Fernandez², George J. Ryan¹, Juila G. Kim³, David C. Majure⁴, Frances M. Alba⁵, Reza Ehsanian^1,*

Canadian Journal of Urology, Vol.33, No.2, pp. 427-440, 2026, DOI:10.32604/cju.2025.070606 - 20 April 2026

Abstract Background: Individuals with spinal cord injury (SCI) are at high risk for developing neurogenic bladder or neurogenic lower urinary tract dysfunction (NLUTD), which can lead to severe complications and negatively impact quality of life. Despite the critical need for timely urologic care, barriers to access remain poorly understood, particularly in resource-limited settings. This study aims to identify systemic and perceived barriers to urologic follow-up for individuals with SCI treated at an academic medical center. Methods: A single-center, observational study was conducted on individuals presenting with a diagnosis code indicative of complete SCI at an academic… More >