Lymphedema is a chronic inflammatory disease that causes chronic swelling in the affected area, necessitating daily treatment. Millions of people worldwide are affected. The investigation of strategies to improve the overall health of patients, such as through the utilisation of electronic health (eHealth), is justified considering the ongoing burden of daily self-care. This research aimed to (a) identify current published research in eHealth and mobile health (mHealth) interventions for patients living with lymphedema; (b) assess feasibility and efficacy of the interventions; and (c) understand whether intervention adherence was affected by using eHealth. A systematic review was undertaken. Seven databases including MEDLINE, Scopus, Web of Science, CINAHL, the Cochrane Library, PsycINFO and IEEE Xplore were searched. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were used. 1857 studies were identified through the database search with 9 meeting the inclusion criteria for a total of 1031 participants. There were 3 types of eHealth, including instructive online content, telehealth, and digital gaming. The efficacy of various eHealth and mHealth modalities was demonstrated in areas such as lymphedema outcomes, self-care, psychosocial outcomes, and disease comprehension. Reports of feasibility demonstrated that eHealth modalities were generally well accepted or preferred over conventional methods. 7 studies reported or discussed adherence and provided insight into the relationship between the design of the eHealth tool and the completion of the intervention. Several distinct categories of eHealth and mHealth interventions were shown to improve disease comprehension, psychosocial and lymphedema outcomes. Findings from this systematic review may have an impact on the design of future studies in this domain, including consideration of early user acceptance testing when developing eHealth tools. With the ongoing progress in eHealth technology, further investigation into eHealth is warranted given the encouraging results observed in a limited number of studies.
Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. We utilized 3762 MRI images alongside Light Gradient Boosting Machine (LightGBM), AdaBoost, gradient boosting, Random Forest, Quadratic Discriminant Analysis, Linear Discriminant Analysis, logistic regression, and transfer learning algorithms. Numerical data was processed with LightGBM, achieving an accuracy of 95.7 %. Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform.
The emergence of the Internet of Things (IoT) has sparked a profound transformation in the field of digital health, leading to the rise of the Internet of Medical Things (IoMT). These IoT applications, while promising significant enhancements in patient care and health outcomes, simultaneously present a myriad of ethical dilemmas. This paper aims to address these ethical challenges by introducing the Adaptive Ethical Framework for IoT in Digital Health (AEFIDH), a comprehensive evaluation framework designed to examine the ethical implications of IoT technologies within digital health contexts. The AEFIDH is developed using a mixed-methods approach, encompassing expert consultations, surveys, and interviews. This approach was employed to validate and refine the AEFIDH, ensuring it encapsulates critical ethical dimensions, including data privacy, informed consent, user autonomy, algorithmic fairness, regulatory compliance, ethical design, and equitable access to healthcare services. The research reveals pressing issues related to data privacy, security, and user autonomy and highlights the imperative need for an increased focus on algorithmic transparency and the integration of ethical considerations in the design and development of IoT applications. Despite certain limitations, the AEFIDH provides a promising roadmap for guiding the responsible development, deployment, and utilization of IoT technologies in digital health, ensuring its relevance amidst the rapidly evolving digital health landscape. This paper contributes a novel, dynamic framework that encapsulates current ethical considerations and is designed to adapt to future technological evolutions, thereby fostering ethical resilience in the face of ongoing digital health innovation. The framework’s inherent adaptability allows it to evolve in tandem with technological advancements, positioning it as an invaluable tool for stakeholders navigating the ethical terrain of IoT in healthcare.
Accurate disease monitoring is an extremely time-consuming task for medical experts and technocrats involved, requiring technical support for diagnostic systems. To overcome this situation, we developed an Internet of Medical Things (IoMT) based on Tsukamoto Type 2 Fuzzy Inference System (TT2FIS) that can easily handle diagnostic and predictive aspects in the medical field. In the proposed system, we developed a Tsukamoto type 2 fuzzy inference system that takes the patient’s symptoms as input factors and the medical device as the output factor of the result. The aim of this work is to demonstrate the usefulness of type 2 fuzzy sets in Tuberculosis and Alzheimer’s disease diagnostic system. Numerical calculations are also performed to illustrate the applicability of the proposed method. A validation of the proposed derivation of the proposed IoMT model is also discussed in the results and conclusions section.
Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.
Atrial fibrillation (AF) is a major public health problem with high rates of morbidity, disability and mortality, especially in the elderly population. This study explored the diagnosis and treatment status of AF in adults aged ≥65 years in the community through wearable dynamic electrocardiogram (ECG) monitoring.
We conducted a cross-sectional study in 4 random communities within the Qingpu district of Shanghai, China. Between January 1, 2020 and June 30, 2022, the ECGs of 3852 adults aged 65 years or older were examined through wearable dynamic ECG monitoring. Data from 3839 participants were ultimately analyzed. Multivariate logistic regression was used to determine the independent predictors of AF.
Wearable dynamic ECG monitoring detected AF in 360 elderly people, 78 of whom were diagnosed with AF for the first time. Multivariate logistic regression analysis revealed that snoring, renal dysfunction, coronary heart disease and high CHA2DS2-VASc score were independent risk factors for AF. Among patients with unknown AF, 68 (87.20 %) met the criteria for anticoagulant therapy based on the CHA2DS2-VASc score. Only 4 (5.88 %) patients were taking anticoagulants. Of the patients with a clear history of AF, 249 (84.98 %) needed an anticoagulant strategy, but only 18 (7.23 %) took oral anticoagulants.
Many elderly people have silent AF, and wearable dynamic ECG monitoring can be used to screen for AF effectively.
The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed.
A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed.
The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment.
To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement
In the realm of intensive care medicine, wearable electronic devices have emerged as a highly promising field, driven by advancements in mobile, intelligent, and personalized healthcare. They are defined as devices that can be worn directly on the body, offering portable services by actively recording physiological parameters and metabolic status, providing index monitoring, clinical diagnosis, and disease treatment. This review specifically highlights the utilization of wearable devices in intensive care units within the field of intensive care medicine, anticipating their future applications.
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