The internet of medical things and artificial intelligence: trends, challenges, and opportunities

Effects of mobile Internet use on the health of middle-aged and older adults: evidences from China health and retirement longitudinal study

The rapid development of digital technology has radically changed people's lives. Simultaneously, as the population is rapidly aging, academic research is focusing on the use of Internet technology to improve middle-aged and older people's health, particularly owing to the popularity of mobile networks, which has further increased the population's accessibility to the Internet. However, related studies have not yet reached a consensus. Herein, empirical analysis of the influence of mobile Internet use on the subjective health and chronic disease status of individuals in their Middle Ages and above was conducted utilizing ordered logit, propensity score matching (PSM), and ordered probit models with data from the 2020 China Health and Retirement Longitudinal Study. The study aimed to provide a theoretical basis and reference for exploring technological advances to empower the development of a healthy Chinese population and to advance the process of healthy aging. The health of middle-aged and older adults mobile Internet users was greatly improved, according to our findings. Further, the use of mobile Internet by these persons resulted in improvements to both their self-assessed health and the state of their chronic diseases. As per the findings of the heterogeneity analysis, the impact of mobile Internet use was shown to be more pronounced on the well-being of middle-aged persons aged 45-60 years compared to those aged ≥ 60 years. Further, the endogeneity test revealed that the PSM model could better eliminate bias in sample selection. The results suggest that the estimates are more robust after eliminating endogeneity, and that failure to disentangle sample selectivity bias would overestimate not only the facilitating effect of mobile Internet use on the self-assessed health impacts of middle-aged and older adults, but also the ameliorating effect of mobile Internet use on the chronic diseases of middle-aged and older adults. The results of the mechanistic analysis suggest that social engagement is an important mediating mechanism between mobile Internet use and the health of middle-aged and older adults. This implies that mobile Internet use increases opportunities for social participation among middle-aged and older adults, thereby improving their health.

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review

BACKGROUND

With the development of emerging technologies, digital behavior change interventions (DBCIs) help to maintain regular physical activity in daily life.

OBJECTIVE

To comprehensively understand the design implementations of habit formation techniques in current DBCIs, a systematic review was conducted to investigate the implementations of behavior change techniques, types of habit formation techniques, and design strategies in current DBCIs.

METHODS

The process of this review followed the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. A total of 4 databases were systematically searched from 2012 to 2022, which included Web of Science, Scopus, ACM Digital Library, and PubMed. The inclusion criteria encompassed studies that used digital tools for physical activity, examined behavior change intervention techniques, and were written in English.

RESULTS

A total of 41 identified research articles were included in this review. The results show that the most applied behavior change techniques were the self-monitoring of behavior, goal setting, and prompts and cues. Moreover, habit formation techniques were identified and developed based on intentions, cues, and positive reinforcement. Commonly used methods included automatic monitoring, descriptive feedback, general guidelines, self-set goals, time-based cues, and virtual rewards.

CONCLUSIONS

A total of 32 commonly design strategies of habit formation techniques were summarized and mapped to the proposed conceptual framework, which was categorized into target-mediated (generalization and personalization) and technology-mediated interactions (explicitness and implicitness). Most of the existing studies use the explicit interaction, aligning with the personalized habit formation techniques in the design strategies of DBCIs. However, implicit interaction design strategies are lacking in the reviewed studies. The proposed conceptual framework and potential solutions can serve as guidelines for designing strategies aimed at habit formation within DBCIs.

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

Light-M: An efficient lightweight medical image segmentation framework for resource-constrained IoMT

The Internet of Medical Things (IoMT) is being incorporated into current healthcare systems. This technology intends to connect patients, IoMT devices, and hospitals over mobile networks, allowing for more secure, quick, and convenient health monitoring and intelligent healthcare services. However, existing intelligent healthcare applications typically rely on large-scale AI models, and standard IoMT devices have significant resource constraints. To alleviate this paradox, in this paper, we propose a Knowledge Distillation (KD)-based IoMT end-edge-cloud orchestrated architecture for medical image segmentation tasks, called Light-M, aiming to deploy a lightweight medical model in resource-constrained IoMT devices. Specifically, Light-M trains a large teacher model in the cloud server and employs computation in local nodes through imitation of the performance of the teacher model using knowledge distillation. Light-M contains two KD strategies: (1) active exploration and passive transfer (AEPT) and (2) self-attention-based inter-class feature variation (AIFV) distillation for the medical image segmentation task. The AEPT encourages the student model to learn undiscovered knowledge/features of the teacher model without additional feature layers, aiming to explore new features and outperform the teacher. To improve the distinguishability of the student for different classes, the student learns the self-attention-based feature variation (AIFV) between classes. Since the proposed AEPT and AIFV only appear in the training process, our framework does not involve any additional computation burden for a student model during the segmentation task deployment. Extensive experiments on cardiac images and public real-scene datasets demonstrate that our approach improves student model learning representations and outperforms state-of-the-art methods by combining two knowledge distillation strategies. Moreover, when deployed on the IoT device, the distilled student model takes only 29.6 ms for one sample at the inference step.

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

Trends in using deep learning algorithms in biomedical prediction systems

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

Using artificial intelligence to improve public health: a narrative review

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson's health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons' physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.

A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered.

Body composition predicts hypertension using machine learning methods: a cohort study

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.

Identification of Clinical Features Associated with Mortality in COVID-19 Patients

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.

ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active learning. This is achieved through selective query of challenging samples for labeling. To the best of our knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers determine whether a patient's three main coronary arteries are stenotic or not. The fourth classifier predicts whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The training is performed once more using the samples labeled so far. The interleaved phases of labeling and training are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the three main coronary arteries as a sample label and considering the two remaining arteries as sample features.

5G Technology in Healthcare and Wearable Devices: A Review

Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.

Swarm Intelligence in Internet of Medical Things: A Review

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

An IoT-Based Deep Learning Framework for Real-Time Detection of COVID-19 through Chest X-ray Images

Over the next decade, Internet of Things (IoT) and the high-speed 5G network will be crucial in enabling remote access to the healthcare system for easy and fast diagnosis. In this paper, an IoT-based deep learning computer-aided diagnosis (CAD) framework is proposed for online and real-time COVID-19 identification. The proposed work first fine-tuned the five state-of-the-art deep CNN models such as Xception, ResNet50, DenseNet201, MobileNet, and VGG19 and then combined these models into a majority voting deep ensemble CNN (DECNN) model in order to detect COVID-19 accurately. The findings demonstrate that the suggested framework, with a test accuracy of 98%, outperforms other relevant state-of-the-art methodologies in terms of overall performance. The proposed CAD framework has the potential to serve as a decision support system for general clinicians and rural health workers in order to diagnose COVID-19 at an early stage.

Triboelectric and Piezoelectric Nanogenerators for Self-Powered Healthcare Monitoring Devices: Operating Principles, Challenges, and Perspectives

The internet of medical things (IoMT) is used for the acquisition, processing, transmission, and storage of medical data of patients. The medical information of each patient can be monitored by hospitals, family members, or medical centers, providing real-time data on the health condition of patients. However, the IoMT requires monitoring healthcare devices with features such as being lightweight, having a long lifetime, wearability, flexibility, safe behavior, and a stable electrical performance. For the continuous monitoring of the medical signals of patients, these devices need energy sources with a long lifetime and stable response. For this challenge, conventional batteries have disadvantages due to their limited-service time, considerable weight, and toxic materials. A replacement alternative to conventional batteries can be achieved for piezoelectric and triboelectric nanogenerators. These nanogenerators can convert green energy from various environmental sources (e.g., biomechanical energy, wind, and mechanical vibrations) into electrical energy. Generally, these nanogenerators have simple transduction mechanisms, uncomplicated manufacturing processes, are lightweight, have a long lifetime, and provide high output electrical performance. Thus, the piezoelectric and triboelectric nanogenerators could power future medical devices that monitor and process vital signs of patients. Herein, we review the working principle, materials, fabrication processes, and signal processing components of piezoelectric and triboelectric nanogenerators with potential medical applications. In addition, we discuss the main components and output electrical performance of various nanogenerators applied to the medical sector. Finally, the challenges and perspectives of the design, materials and fabrication process, signal processing, and reliability of nanogenerators are included.

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.