Smart Health最新文献

{"title":"iSecureHealth: An efficient and secure technique to exchange health data using IoMT devices","authors":"Chayan Kanti Dhar,&nbsp;Abhishek Majumder","doi":"10.1016/j.smhl.2024.100504","DOIUrl":"10.1016/j.smhl.2024.100504","url":null,"abstract":"<div><p>The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT), which consists of internet-connected medical devices, hardware, and software applications that facilitate healthcare information technology. Transformation of the healthcare sector through the adoption of IoMT devices offers significant benefits, including efficient and timely medical interventions based on real-time monitoring of patients’ vitals. Security, authentication and privacy safeguards are the key hurdles in adopting medical-grade IoMT deployment. To address these critical hurdles, a lightweight, efficient and reliable key exchange scheme, termed iSecureHealth, has been proposed. The proposed system incorporates a security control node outside the User-IoMT-Gateway paradigm to enforce end-to-end secure data transactions for a medical-grade IoMT-based patient monitoring Environment. The secure data transaction techniques and key management comprise an authentication, authorization, and access (AAA) control layer, ensuring a secure data channel between IoMT sensors and the Gateway node (GNo) paradigm. Elliptic Curve Cryptography (ECC)-based key management, using the Elliptic Curve Diffie–Hellman Key Exchange technique, provides a secure, end-to-end private health data transmission through authorized IoMT devices. We used HMACSHA256 for JWT session key generation to design a lightweight automatic authentication scheme for iSecureHealth. For mutual authentication validation, a well-known BAN-Logic is applied. We considered the widely accepted random Oracle-based Real-Or-Random (ROR) model and Dolev–Yao (DY) logic for formal and informal security analysis, respectively. A generic ESP32/ESP-32S development board connected with a multisensory (MAX30102) was used for implementation. The publisher–subscriber-based lightweight Secure Message Queuing Telemetry Transport (SMQTT) protocol demonstrates real-time streaming of sensor-acquired data over the secure transport layer. Our experiments and results show that the performance of the proposed technique is better compared to the baselines.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100504"},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of medical artificial intelligence technology in sub-Saharan Africa: Prospects for medical laboratories","authors":"Richard Kobina Dadzie Ephraim ,&nbsp;Gabriel Pezahso Kotam ,&nbsp;Evans Duah ,&nbsp;Frank Naku Ghartey ,&nbsp;Evans Mantiri Mathebula ,&nbsp;Tivani Phosa Mashamba-Thompson","doi":"10.1016/j.smhl.2024.100505","DOIUrl":"10.1016/j.smhl.2024.100505","url":null,"abstract":"<div><p>The widespread adoption of artificial intelligence (AI) technology globally has brought significant changes to various sectors. AI-assisted algorithms have notably improved decision-making, operational efficiency, and productivity, especially in healthcare and medicine. However, in low and middle-income countries (LMICs), particularly in sub-Saharan Africa (SSA), the integration of medical AI has faced delays and challenges, slowing its acceptance and implementation in medical interventions. This thematic narrative critically explores the current trends and patterns in applying medical AI in SSA, with a specific focus on its potential impact on medical laboratories. The review covers the general use of medical AI in SSA, examining factors like enablers, challenges, and opportunities that influence healthcare systems. Additionally, it looks into the implications of medical AI for medical laboratories and suggests context-specific and practical recommendations for potential integration. We highlight various challenges, including data availability, security concerns, resource limitations, regulatory gaps, poor internet connectivity, and digital literacy issues, contributing to the slow integration of AI in healthcare systems in SSA. Despite challenges, the adoption of medical AI in SSA medical laboratories holds latent potential for improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Further exploration and careful consideration are necessary to unlock these possibilities.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000618/pdfft?md5=5ebc9f63c766918d348d9c6ec4b33b87&pid=1-s2.0-S2352648324000618-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards accurate Diabetic Foot Ulcer image classification: Leveraging CNN pre-trained features and extreme learning machine","authors":"Fitri Arnia ,&nbsp;Khairun Saddami ,&nbsp;Roslidar Roslidar ,&nbsp;Rusdha Muharar ,&nbsp;Khairul Munadi","doi":"10.1016/j.smhl.2024.100502","DOIUrl":"10.1016/j.smhl.2024.100502","url":null,"abstract":"<div><p>Diabetes mellitus (DM) can cause irreversible tissue damage in the legs, leading to foot ulcers that are difficult to heal. Early detection is crucial in preventing further complications. This study proposes a detection system for foot ulcers using a hybrid approach that combines deep convolutional neural networks (CNN) with an extreme learning machine (ELM). We explore the features of popular pre-trained models, including ResNet101, DenseNet201, MobileNetv2, EfficientNetB0, InceptionResNetv2, and NasNet mobile. Given the challenge of a limited dataset, traditional data augmentation may introduce inter-class bias. Therefore, we adopt a fusion of CNN and ELM to mitigate this issue. The experiments show promising results, with ResNet101, DenseNet201, InceptionResNetv2, MobileNetV2, NasNet mobile, and EfficientNetB0 achieving accuracies of 80%, 76.67%, 80%, 83.34%, 80%, and 80%, respectively. Our analysis reveals that MobileNetV2 provides the best feature representation, achieving the highest accuracy rate of 83.34% with zero false positives. Based on the findings, we suggest that the proposed hybrid method can accurately recognize DM foot images, providing a potential tool for early diagnosis and treatment of foot ulcers.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100502"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000588/pdfft?md5=fd85ad0912474ec33c2bdf458506b97e&pid=1-s2.0-S2352648324000588-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amsel criteria based computer vision for diagnosing bacterial vaginosis","authors":"Daniel Highland,&nbsp;Gang Zhou","doi":"10.1016/j.smhl.2024.100501","DOIUrl":"10.1016/j.smhl.2024.100501","url":null,"abstract":"<div><p>Bacterial vaginosis (BV) is a common vaginal infection that can predispose patients to several complications, such as pelvic inflammatory disease. Like many illnesses, existing diagnostic methods face a trade-off between diagnostic certainty and cost. To help address this dilemma, we explore a computational diagnostic approach implementable as an IoT device. We developed several deep learning models based on the Amsel criteria to evaluate different inexpensive point-of-care tests that better automate the diagnosis of BV. We first determined how to best diagnose BV via computer vision models trained on epithelial cell images. We found that training a ResNet18 model on NuSwab diagnostic labels achieved an 89% F1 score. We then experimented with augmenting computer vision results with other Amsel criteria values through multi-layer perceptrons, finding that also using whiff test values increased performance to an F1 of 91% and to a sensitivity surpassing human-performed Amsel criteria at 94.31%. These results provide the first insight into how combinations of images and other Amsel criteria data can best be used for reliable diagnoses, paving the way for future research into IoT-based BV diagnostics.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100501"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying ambulation patterns in institutional settings","authors":"Jose-Valentin Sera-Josef ,&nbsp;Joseph J. LaViola ,&nbsp;Mary Elizabeth Bowen","doi":"10.1016/j.smhl.2024.100503","DOIUrl":"10.1016/j.smhl.2024.100503","url":null,"abstract":"<div><p>Observational studies of older adults show pacing, lapping, and stationary ambulation patterns can be associated with an increased risks for falls or an early sign of an acute or other health event in long-term care. The aim of this study is to use classical machine learning algorithms to automate the process of recognizing these patterns with the goal of assisting health care staff in monitoring the health and well-being of their residents. This study utilized data from six residents whose movements were tracked with a real-time locating system while performing everyday activities of daily living for up to 1.9 years. No residents exhibited lapping patterns over the course of the study. Machine learning statistical techniques recognized stationary and pacing with accuracy≥70%, with indirect and direct patterns having an accuracy of around 50% due to environmental constraints. Study findings suggest automated methods may be used with real-time locating data to recognize ambulation patterns that have been associated with poor health in this population. Study findings may be utilized by health care staff to tailor resident care plans and develop timely interventions that may affect falls and provide for the earlier recognition of acute events in this population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100503"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable SE-MobileNet for Pneumonia detection integrated with robustness assessment using adversarial examples","authors":"Somak Saha ,&nbsp;Chamak Saha ,&nbsp;Mohammad Zavid Parvez ,&nbsp;Md Tanzim Reza","doi":"10.1016/j.smhl.2024.100500","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100500","url":null,"abstract":"<div><p>Pneumonia is a detrimental disease, especially for children, which is caused due to bacterial infection. X-ray images are frequently observed manually to find out the existence of pneumonia in a patient’s body. However, diagnosing pneumonia using X-ray images through manual observation by different health professionals may lead to different conclusions. Thus, an efficient autonomic system is required to diagnose pneumonia from X-ray images, and deep learning techniques, such as CNN-based approaches are frequently used to create such autonomy. To ease the process of pneumonia diagnosis, in this study, we have proposed the SE-MobileNet approach. We compared the performance of the proposed SE-MobileNet with the default version of MobileNetV2 integrated with transfer learning. Using the publicly available Kaggle dataset, it is observed that SE-MobileNet obtained 97.4% accuracy on a select test set against the 96.4% accuracy of MobileNetV2, and in 10-fold cross-validation, SE-MobileNet achieved an average of 95.92% accuracy against the 92.35% accuracy of MobileNetV2. Further comparison analysis proves that the SE-MobileNet model not only performs much better than the vanilla MobileNetV2 but also performs competitively against the literature. In addition, robustness evaluation has been introduced in this study where Fast Gradient Sign Method (FGSM) is performed to generate adversarial images. It is found that in robustness evaluation, SE-MobileNet also performs better compared to MobileNetV2. Finally, to validate the appropriateness of the learning of the model, Explainable AI (XAI) based techniques have been employed.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100500"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons","authors":"Jingxiao Tian ,&nbsp;Patrick Mercier ,&nbsp;Christopher Paolini","doi":"10.1016/j.smhl.2024.100498","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100498","url":null,"abstract":"<div><p>This work focuses on the development and manufacturing of a wireless, wearable, low-power fall detection sensor (FDS) designed to predict and detect falls in elderly at-risk individuals. Unintentional falls are a significant risk in this demographic, often resulting from diminished physical capabilities such as reduced hand grip strength and complications from conditions like arthritis, vertigo, and neuromuscular issues. To address this, we utilize advanced low-power field-programmable gate arrays (FPGAs) to implement a fixed-function neural network capable of categorizing activities of daily life (ADLs), including the detection of falls. This system employs a Convolutional Neural Network (CNN) model, trained and validated using the Caffe deep learning framework with data collected from human subjects experiments. This system integrates an ST Microelectronics LSM6DSOX inertial measurement unit (IMU) sensor, embedded with an ultra-low-power Lattice iCE40UP FPGA, which samples and stores joint acceleration and orientation rate. Additionally, we have acquired and published a dataset of 3D accelerometer and gyroscope measurements from predefined ADLs and falls, using volunteer human subjects. This innovative approach aims to enhance the safety and well-being of older adults by providing timely and accurate fall detection and prediction.</p><p>In this paper, we present an innovative approach to utilizing a compact Convolutional Neural Network (CNN) core for accelerating convolutional operations on a machine learning model, suitable for deployment on an ultra-low power FPGA.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000540/pdfft?md5=436ed873a93f5155977c1d04d19122da&pid=1-s2.0-S2352648324000540-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital native students using nutritional apps: are they more adherent to a mediterranean diet model? Results from the good APPetite survey","authors":"Silvia Marconi,&nbsp;Elisa Carrara,&nbsp;Giulia Gilberti,&nbsp;Maurizio Castellano,&nbsp;Barbara Zanini","doi":"10.1016/j.smhl.2024.100497","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100497","url":null,"abstract":"<div><p>Reading and understanding food labels are crucial steps in healthy dietary choices. Nutrition-related applications (n-apps) have increased in the recent years and the aim of this study was to assess the use and the perception of n-apps among a population of university students, also investigating the attitude and relationship with reading food labels and adherence to the Mediterranean diet (Medi-Lite score).</p><p>In 2023, 316 students, mainly attending the courses of Medicine, Pharmacy and Dietetics at the University of Brescia, Italy, completed an anonymous and specifically designed survey. 33.9% of the students stated that they use or have used n-apps. The most used apps were Yuka, MyFitnessPal, Fat Secret and Yazio, especially for the ease of use, speed, nutritional values estimation and barcode reading. 53.2% and 53.5% of the students declared to be food information and nutrition label readers respectively and the Medi-Lite mean value was 9.98 ± 2.46. N-app-users were significantly more attentive to food information and nutrition label than app not-users (both p &lt; 0.0001) and recorded a Medi-Lite score significantly higher (p = 0.0131).</p><p>The present study found for the first time an extensive correlation between the use of n-apps, the food labels awareness and healthy eating habits in a digitally native population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100497"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of an AI-driven personal health platform on cardiovascular disease risk","authors":"Craig McNulty ,&nbsp;Justin Holland ,&nbsp;Cameron McDonald ,&nbsp;Marshall J. McGee","doi":"10.1016/j.smhl.2024.100499","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100499","url":null,"abstract":"<div><p>Cardiovascular disease (CVD) is a global health concern, particularly among older adults, linked to fat and sugar-rich diet, inactivity, tobacco smoking, as well as genetic factors. Regular exercise and a balanced diet are recommended to reduce CVD risk. Recently, the use of computer of mobile phone application platforms have become promenent tools for personal health interventions and guidance. Although research findings of the efficacy of mobile health apps has had mixed results, improved user health and lowered risk of CVD has been seen with regular engagement. Shae, an AI-driven mobile health platform, offers personalized lifestyle guidance based on an individual's phenotype. The aim of this study is to assess whether engagement with the online platform, Shae, decreases Framingham non-laboratory CVD risk score over time.</p><p>The study collected data from 1684 participants who engaged with the Shae online platform for at least 3-months between May 2014 and March 2018. After applying exclusion criteria, 5225 complete assessment entries remained for analysis. The Shae platform assessed health, predicted future risks, and recommended personalized interventions. Data included anthropometric measurements and survey responses, converted for standardized analysis. Descriptive statistics were calculated for participants' characteristics and compared across gender and age groups, in alignment with established cardiovascular disease (CVD) risk factors. A linear mixed-effect regression model was employed to assess changes in CVD risk over time. This research aimed to evaluate the impact of personalized health interventions on CVD risk in diverse demographic groups.</p><p>CVD risk score, when adjusted for age, decreased by 11.2% following 24 months of engagement with the Shae app, with men 45 years and older seeing the largest decrease (16.1%), and the lowest decrease seen in women under 55 years (10%). Use of the Shae app was primarily by women (85%).</p><p>This study assessed the impact of engagement with the mHealth app, Shae, on reducing CVD risk. Results revealed a significant CVD risk reduction, notably in men over 45. mHealth apps like Shae provide personalized health assessments, activity tracking, nutrition advice, and medication reminders, enabling proactive cardiovascular health management. Increased physical activity and better blood pressure control were key factors in risk reduction. Tailored interventions for high-risk groups align with previous research. Given the importance of preventing CVD, mHealth apps hold promise. This 24-month study offers robust insights, and the focus on individual phenotypes enhances engagement and health outcomes.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000552/pdfft?md5=f00ef3c52ea6e6893fd73aa926a9eafe&pid=1-s2.0-S2352648324000552-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APC: Contactless healthy sitting posture monitoring with microphone array","authors":"Kaiyuan Ma ,&nbsp;Shunan Song ,&nbsp;Lingling An ,&nbsp;Shiwen Mao ,&nbsp;Xuyu Wang","doi":"10.1016/j.smhl.2024.100463","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100463","url":null,"abstract":"<div><p>The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100463"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}