Smart Health最新文献

{"title":"Privacy-enhanced heart stroke detection using Federated Learning and Homomorphic Encryption","authors":"Vankamamidi S. Naresh ,&nbsp;Gadhiraju Tej Varma","doi":"10.1016/j.smhl.2025.100594","DOIUrl":"10.1016/j.smhl.2025.100594","url":null,"abstract":"<div><div>Heart stroke detection plays a vital role in early diagnosis and intervention leveraging machine learning (ML) models to predict stroke events from medical data. However, the use of such models often faces significant challenges related to data privacy and security, especially when sensitive health information is involved. To tackle these concerns, we present an innovative privacy-preserving approach for heart stroke detection using Federated Learning (FL) combined with Homomorphic Encryption (HE) utilizing the Cheon-Kim-Kim-Song (CKKS) scheme. FL is employed to enable edge nodes to locally train a Feed Forward Neural Network (FFNN) model on heart stroke data, without transferring any raw patient data to a central server, thus ensuring the data remains decentralized and private. During the communication between edge nodes and central server, CKKS encryption ensures that model updates remain encrypted throughout the aggregation process, allowing computations to be performed without decryption, thus enhancing privacy and security. Furthermore, the FedAvg is incorporated to aggregate model updates efficiently, ensuring robust model training while maintaining decentralized data integrity. Our model achieved an average accuracy of over 95 % in predicting heart stroke events, demonstrating the effectiveness of combining FL with an FFNN in terms of both performance and privacy. This approach allows the utilization of rich medical data for training while maintaining the security and confidentiality of sensitive health information, making it an effective option for real-world healthcare applications in contexts where privacy and security of data is of the utmost importance.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100594"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184264","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":"An interpretable deep learning framework for predicting hospital readmissions from electronic health records","authors":"Fabio Azzalini ,&nbsp;Tommaso Dolci ,&nbsp;Marco Vagaggini","doi":"10.1016/j.smhl.2025.100581","DOIUrl":"10.1016/j.smhl.2025.100581","url":null,"abstract":"<div><div>With the increasing availability of patient data, modern medicine is shifting towards prospective healthcare. Electronic health records offer a variety of information useful for clinical patient characterization and the development of predictive models, given that similar medical histories often lead to analogous health progressions. One application is the prediction of unplanned hospital readmissions, an essential task for reducing healthcare costs and improving patient outcomes. While predictive models demonstrate strong performances especially with deep learning approaches, they are often criticized for their lack of interpretability, a critical requirement in the medical domain where incorrect predictions may have severe consequences for patient safety. In this paper, we propose a novel and interpretable deep learning framework for predicting unplanned hospital readmissions, supported by NLP findings on word embeddings and by ConvLSTM neural networks for better handling temporal data. We validate the framework on two predictive tasks for hospital readmission within 30 and 180 days, using real-world data. Additionally, we introduce and evaluate a model-dependent technique designed to enhance result interpretability for medical professionals. Our solution outperforms traditional machine learning models in prediction accuracy while simultaneously providing more interpretable results.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100581"},"PeriodicalIF":0.0,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147537","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":"Technoethics and recommendations for technological interventions to reduce loneliness in older adults","authors":"Emma Cho,&nbsp;Connie M. Ulrich","doi":"10.1016/j.smhl.2025.100583","DOIUrl":"10.1016/j.smhl.2025.100583","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116780","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":"Smart implantable devices for cardiac health: A novel self-powered wireless ECG monitoring system using energy harvesting and machine learning-driven anomaly detection","authors":"Khadija Pervez ,&nbsp;Muhammad Izhar ,&nbsp;Adeel Ahmed ,&nbsp;Nazik Alturki ,&nbsp;Saima Abdullah","doi":"10.1016/j.smhl.2025.100582","DOIUrl":"10.1016/j.smhl.2025.100582","url":null,"abstract":"<div><div>Introduction of smart implantable devices is changing the face of cardiac health monitoring through continuous and real time ECG monitoring useful in early diagnosis of cardiac pathologies. This paper describes CardioHarvest-Net, a newly developed self-powered wireless ECG monitoring system that employs, physiological movements for its power in order to reduce the probability of frequent power replenishment. This self-powered capability eliminates dependency on conventional batteries, thereby offering a viable solution for continuous, long-term cardiac monitoring in real-world conditions. CardioHarvest-Net enables an enhanced machine learning (ML)-based anomaly detection model that learns and adapt to each patient's cardiac behavior to provide high sensitivity in abnormal ECG signs related to diseases like arrhythmia, myocardial infarction, and other diseases of the heart. The CardioHarvest-Net model applies CNN for feature extraction of vital signs such as ECG and uses LSTM for temporal feature extraction for accurate anomaly detection in real-world settings. Evaluation results reveal that gain scores of cardio health phenomena via CardioHarvest-Net is a detection accuracy of 97.2 % and the anomaly recall rate of 95.3 % that qualifies the proposed system as an effective and timely monitoring tool of putting up a signal and cautionary measure on possible event of cardiac occurrences. The average response time for an entire system to detect an anomaly is 10 ms, which makes the system's intervention capacity rather fast. Moreover, they use a power build-up efficiency of 78 % in otherwise low power, real-life in-vivo conditions ranging from acute circumstances to chronic conditions requiring prolonged operation. This ML model is running on an energy-efficient microcontroller suitable for wearable and implantable medical devices along with a feedback adaptation that enhances the accuracy of the predictions based on data that changes over time concerning an individual patient. The outcomes of this study further state the viability of CardioHarvest-Net to transform sustainable cardiac niche by addressing limitations into power independence and facilitating real-time tracking. This development is a breakthrough in moving towards the preventive, long-term approach to cardiac reliability enhancing our method in a manner that offers a solid framework for constant, individualized cardiac monitoring, and timely action in cases of essential occurrences in the heart.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"37 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068558","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":"Clinically relevant predictive modeling for personalized ACL reconstruction classification","authors":"Xishi Zhu ,&nbsp;Ryan Henry ,&nbsp;Emily Jackson ,&nbsp;Joe M. Hart ,&nbsp;Jiaqi Gong","doi":"10.1016/j.smhl.2025.100575","DOIUrl":"10.1016/j.smhl.2025.100575","url":null,"abstract":"<div><div>Anterior Cruciate Ligament (ACL) reconstruction outcomes and return-to-sport readiness vary significantly among patients, yet current classification methods often lack interpretability and personalization. We propose an explainable predictive model for ACL reconstruction classification through multi-modal analysis of gait dynamics and patient characteristics. Using inertial measurement unit (IMU) sensors on participants’ wrists, ankles, and sacrum, we collected gait data during walking and jogging tasks, alongside patient-specific survey information. For gait dynamics, we employed Phase Slope Index to quantify inter-sensor relationships and trained classifiers for different ACL reconstruction outcomes(left vs right injury, healthy vs injured), achieving high classification performance (96.37% accuracy). Model explanations using heatmaps and permutation importance revealed that paired body movements are crucial in classification, with more distinct patterns in jogging than walking. For patient characteristics, t-SNE visualization demonstrated that model confidence correlated strongly with recovery duration. While longer recovery typically leads to more normal gait patterns, our approach provides a quantitative method to visualize this process transparently. This explainable, personalized approach can improve rehabilitation strategies and inform more accurate return-to-sport decisions in sports medicine.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100575"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895599","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":"Editorial – Elsevier smart health special issue: Advancing ICT for health, accessibility, and wellbeing","authors":"Achilleas Achilleos ,&nbsp;Edwige Pissaloux ,&nbsp;George A. Papadopoulos ,&nbsp;Ramiro Velazquez","doi":"10.1016/j.smhl.2025.100580","DOIUrl":"10.1016/j.smhl.2025.100580","url":null,"abstract":"","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100580"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947103","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":"Enhancing polyp detection in endoscopy with cross-channel self-attention fusion","authors":"Xiaolong Liang ,&nbsp;Shuijiao Chen ,&nbsp;Linfeng Shu ,&nbsp;Dechun Wang ,&nbsp;Qilei Chen ,&nbsp;Yu Cao ,&nbsp;Benyuan Liu ,&nbsp;Honggang Zhang ,&nbsp;Xiaowei Liu","doi":"10.1016/j.smhl.2025.100578","DOIUrl":"10.1016/j.smhl.2025.100578","url":null,"abstract":"<div><div>Colorectal cancer (CRC) poses a significant global health challenge, ranking as a leading cause of cancer-related mortality. Colonoscopy, the most effective means of preventing CRC, is utilized for early detection and removal of precancerous growths. However, while there have been many efforts that utilize deep learning based approaches for automatic polyp detection, false positive rates in polyp detection during colonoscopy remain high due to the diverse characteristics of polyps and the presence of various artifacts. This paper introduces an innovative technique aimed at improving polyp detection accuracy in colonoscopy video frames. The proposed method introduces a novel framework incorporating a cross-channel self-attention fusion unit, aimed at enhancing polyp detection accuracy in endoscopic procedures. The integration of this unit proves to play an important role in refining prediction quality, resulting in more precise detection outcomes in complex medical imaging scenarios. To substantiate the effectiveness of our framework, we create an extensive private dataset comprising complete endoscopy videos, captured from diverse equipment from different manufacturers. This dataset represents realistic and intricate application scenarios, offering an authentic and effective foundation for both training and evaluating our framework. Thorough experiments and ablation studies are conducted to assess the performance of our proposed approach. The results demonstrate that our framework, featuring key technical innovations, significantly reduces false detections and achieves a higher recall rate. This underscores the remarkable effectiveness of our framework in upgrading polyp detection accuracy in real-world endoscopy procedures.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100578"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850205","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":"Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring","authors":"Almustapha A. Wakili,&nbsp;Babajide J. Asaju,&nbsp;Woosub Jung","doi":"10.1016/j.smhl.2025.100579","DOIUrl":"10.1016/j.smhl.2025.100579","url":null,"abstract":"<div><div>Breath analysis has emerged as a critical tool in health monitoring, offering insights into respiratory function, disease detection, and continuous health assessment. While traditional contact-based methods are reliable, they often pose challenges in comfort and practicality, particularly for long-term monitoring. This survey comprehensively examines contact-based and contactless approaches, emphasizing recent advances in machine learning and deep learning techniques applied to breath analysis. Contactless methods, including Wi-Fi Channel State Information and acoustic sensing, are analyzed for their ability to provide accurate, noninvasive respiratory monitoring.</div><div>We explore a broad range of applications, from single-user respiratory rate detection to multi-user scenarios, user identification, and respiratory disease detection. Furthermore, this survey details essential data preprocessing, feature extraction, and classification techniques, offering comparative insights into machine learning/deep learning models suited to each approach. Key challenges like dataset scarcity, multi-user interference, and data privacy are also discussed, along with emerging trends like Explainable AI, federated learning, transfer learning, and hybrid modeling. By synthesizing current methodologies and identifying open research directions, this survey offers a comprehensive framework to guide future innovations in breath analysis, bridging advanced technological capabilities with practical healthcare applications.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100579"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863368","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":"Thermal vision: Pioneering non-invasive temperature tracking in congested spaces","authors":"Arijit Samal,&nbsp;Haroon R. Lone","doi":"10.1016/j.smhl.2025.100576","DOIUrl":"10.1016/j.smhl.2025.100576","url":null,"abstract":"<div><div>Non-invasive temperature monitoring of individuals plays a crucial role in identifying and isolating symptomatic individuals. Temperature monitoring becomes particularly vital in settings characterized by close human proximity, often referred to as <em>dense settings</em>. However, existing research on non-invasive temperature estimation using thermal cameras has predominantly focused on <em>sparse settings</em>. Unfortunately, the risk of disease transmission is significantly higher in dense settings like movie theaters or classrooms. Consequently, there is an urgent need to develop robust temperature estimation methods tailored explicitly for dense settings.</div><div>Our study proposes a non-invasive temperature estimation system that combines a thermal camera with an edge device. Our system employs YOLO models for face detection and utilizes a regression framework for temperature estimation. We evaluated the system on a diverse dataset collected in dense and sparse settings. Our proposed face detection model achieves an impressive mAP score of over 94 in both in-dataset and cross-dataset evaluations. Furthermore, the regression framework demonstrates remarkable performance with a mean square error of 0.18 °C and an impressive <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score of 0.96. Our experiments’ results highlight the developed system’s effectiveness, positioning it as a promising solution for continuous temperature monitoring in real-world applications. With this paper, we release our dataset and programming code publicly.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100576"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792391","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":"Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications","authors":"Guna Sekaran Jaganathan,&nbsp;Indika Kahanda,&nbsp;Upulee Kanewala","doi":"10.1016/j.smhl.2025.100564","DOIUrl":"10.1016/j.smhl.2025.100564","url":null,"abstract":"<div><div>Healthcare and medical domain-specific LLMs (BioMed LLMs), such as PubMedBERT and Med-PaLM, are developed and pre-trained on biomedical and clinical text to be used specifically in healthcare and medical applications. The recent popularity of these BioMed LLMs increased the use of LLMs in health and medical applications to perform various critical tasks, including ICD (International Classification of Diseases) coding. For such safety-critical applications, it is vital to focus not just on accuracy but also on other quality attributes such as robustness and fairness. Unfortunately, application developers rarely assess these attributes despite their importance in BioMed LLMs-based applications due to difficulties in defining the expected output. This study uses Metamorphic Testing (MT) to evaluate the robustness and fairness of the BioMed LLM-based automated ICD coding application. We defined several Metamorphic Relations (MRs) to evaluate these quality attributes systematically. Our results using the MIMIC-III dataset reveal several instances where the application performance is significantly impacted due to various simple manipulations that mimic common mistakes in the input clinical notes. Our findings highlight the necessity of rigorous testing for these metrics to ensure the reliable use of BioMed LLMs in healthcare and medical applications. Further, our research provides a comprehensive framework for such evaluations by leveraging MT, which is helpful to the application developers and contributes to developing more reliable and robust biomedical AI systems.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100564"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777368","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}