Smart Health最新文献

Clinically relevant predictive modeling for personalized ACL reconstruction classification 个性化ACL重建分类的临床相关预测建模

Smart Health Pub Date : 2025-04-29 DOI: 10.1016/j.smhl.2025.100575

Xishi Zhu , Ryan Henry , Emily Jackson , Joe M. Hart , Jiaqi Gong

{"title":"Clinically relevant predictive modeling for personalized ACL reconstruction classification","authors":"Xishi Zhu , Ryan Henry , Emily Jackson , Joe M. Hart , 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}

引用次数: 0

Editorial – Elsevier smart health special issue: Advancing ICT for health, accessibility, and wellbeing 社论-爱思唯尔智能健康特刊：推进ICT促进健康、可及性和福祉

Smart Health Pub Date : 2025-04-23 DOI: 10.1016/j.smhl.2025.100580

Achilleas Achilleos , Edwige Pissaloux , George A. Papadopoulos , Ramiro Velazquez

引用次数: 0

Enhancing polyp detection in endoscopy with cross-channel self-attention fusion 跨通道自注意融合增强内镜息肉检测

Smart Health Pub Date : 2025-04-17 DOI: 10.1016/j.smhl.2025.100578

Xiaolong Liang , Shuijiao Chen , Linfeng Shu , Dechun Wang , Qilei Chen , Yu Cao , Benyuan Liu , Honggang Zhang , Xiaowei Liu

{"title":"Enhancing polyp detection in endoscopy with cross-channel self-attention fusion","authors":"Xiaolong Liang , Shuijiao Chen , Linfeng Shu , Dechun Wang , Qilei Chen , Yu Cao , Benyuan Liu , Honggang Zhang , 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}

引用次数: 0

Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring 呼吸作为一种生物标志物：在呼吸监测中的接触和非接触应用和方法的调查

Smart Health Pub Date : 2025-04-16 DOI: 10.1016/j.smhl.2025.100579

Almustapha A. Wakili, Babajide J. Asaju, Woosub Jung

{"title":"Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring","authors":"Almustapha A. Wakili, Babajide J. Asaju, 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}

引用次数: 0

Thermal vision: Pioneering non-invasive temperature tracking in congested spaces 热视觉：在拥挤的空间开创性的非侵入性温度跟踪

Smart Health Pub Date : 2025-04-03 DOI: 10.1016/j.smhl.2025.100576

Arijit Samal, Haroon R. Lone

{"title":"Thermal vision: Pioneering non-invasive temperature tracking in congested spaces","authors":"Arijit Samal, 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}

引用次数: 0

Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications 基于llm的自动化ICD编码应用鲁棒性和公平性评估的变质测试

Smart Health Pub Date : 2025-04-02 DOI: 10.1016/j.smhl.2025.100564

Guna Sekaran Jaganathan, Indika Kahanda, Upulee Kanewala

{"title":"Metamorphic Testing for Robustness and Fairness Evaluation of LLM-based Automated ICD Coding Applications","authors":"Guna Sekaran Jaganathan, Indika Kahanda, 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}

引用次数: 0

Dynamic fog computing for enhanced LLM execution in medical applications 在医疗应用中增强LLM执行的动态雾计算

Smart Health Pub Date : 2025-04-02 DOI: 10.1016/j.smhl.2025.100577

Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer

{"title":"Dynamic fog computing for enhanced LLM execution in medical applications","authors":"Philipp Zagar , Vishnu Ravi , Lauren Aalami , Stephan Krusche , Oliver Aalami , Paul Schmiedmayer","doi":"10.1016/j.smhl.2025.100577","DOIUrl":"10.1016/j.smhl.2025.100577","url":null,"abstract":"<div><div>The ability of large language models (LLMs) to process, interpret, and comprehend vast amounts of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises concerns about data privacy and trust in remote LLM platforms. Additionally, the cost of cloud-based artificial intelligence (AI) services remains a barrier to widespread adoption. To address these challenges, we propose shifting the LLM execution environment from centralized, opaque cloud providers to a decentralized and dynamic fog computing architecture. By running open-weight LLMs in more trusted environments, such as a user’s edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial concerns associated with cloud-based LLMs. We introduce <em>SpeziLLM</em>, an open-source framework designed to streamline LLM execution across multiple layers, facilitating seamless integration into digital health applications. To demonstrate its versatility, we showcase <em>SpeziLLM</em> across six digital health applications, highlighting its broad applicability in various healthcare settings.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792390","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}

引用次数: 0

Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation 基于贝叶斯先验估计的异质感知室内定位

Smart Health Pub Date : 2025-03-28 DOI: 10.1016/j.smhl.2025.100559

Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry

{"title":"Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation","authors":"Changming Li , Cong Shi , Yingying Chen , David Maluf , Jerome Henry","doi":"10.1016/j.smhl.2025.100559","DOIUrl":"10.1016/j.smhl.2025.100559","url":null,"abstract":"<div><div>Indoor wireless localization is critical for enabling a wide range of mobile and IoT applications, such as elder monitoring, robot navigation, and augmented reality/virtual reality. Current wireless localization techniques rely on homogeneous sensing, utilizing single-modality signals like Bluetooth, WiFi, or mmWave, which are susceptible to in-channel interference, multipath distortions, and environmental variability (e.g., device position and furniture placement changes). In this paper, we design a heterogeneous sensing system that combines wireless signals of multiple modalities to enhance indoor localization accuracy. Through building a Bayesian-based framework, we statistically integrate location fingerprints from various sensing modalities to address the nonlinearities introduced by spatial and temporal fluctuations. Our approach is generalizable and can be applied to existing fingerprinting localization methods based on machine learning algorithms, such as K-nearest neighbors (KNN), support vector machines (SVM), and deep learning models, significantly enhancing the localization performance and robustness. Extensive real-world experiments demonstrate that our system reduces the average localization errors from 2.1 m to 1.23 m, even in the presence of complex environmental dynamics.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760192","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}

引用次数: 0

Objective anxiety level classification using unsupervised learning and multimodal physiological signals 目的利用无监督学习和多模态生理信号对焦虑水平进行分类

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100572

Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez

{"title":"Objective anxiety level classification using unsupervised learning and multimodal physiological signals","authors":"Maxine He , Jonathan Cerna , Roshni Mathew , Jiaqi Zhao , Jennifer Zhao , Ethan Espina , Jean L. Clore , Richard B. Sowers , Elizabeth T. Hsiao-Wecksler , Manuel E. Hernandez","doi":"10.1016/j.smhl.2025.100572","DOIUrl":"10.1016/j.smhl.2025.100572","url":null,"abstract":"<div><div>Anxiety disorders are prevalent worldwide and can negatively impact physical and mental health. Thus, the timely detection of changes in anxiety levels is crucial for mental health management. This study used multimodal physiological features from wearable devices to classify anxiety levels across various conditions, normalized by individual baseline responses for personalized analysis. Gaussian Mixture Models clustered data into binary or ternary anxiety levels, interpreted by statistics of self-reported scores and physiological features. Clus ters showed modest alignment with State and Trait Inventory scores and physiological markers and demonstrated task-specific variability. Silhouette scores indicated moderate separation (0.40 for two clusters, 0.14 for three clusters). Binary and three-class classifications using unsupervised learning and leave-one-participant-out validation demonstrated effectiveness, with Support Vector Machine achieving highest accuracies (90.9% and 73.3%). This approach enables objective, personalized anxiety monitoring without relying on subjective labeling.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100572"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725637","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}

引用次数: 0

Intoxication detection from speech using representations learned from self-supervised pre-training 使用自监督预训练学习表征的语音中毒检测

Smart Health Pub Date : 2025-03-27 DOI: 10.1016/j.smhl.2025.100562

Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu

{"title":"Intoxication detection from speech using representations learned from self-supervised pre-training","authors":"Abigail Albuquerque, Samuel Chibuoyim Uche, Emmanuel Agu","doi":"10.1016/j.smhl.2025.100562","DOIUrl":"10.1016/j.smhl.2025.100562","url":null,"abstract":"<div><div>Alcohol intoxication is one of the leading causes of death around the globe. Existing approaches to prevent Driving Under the Influence (DUI) are expensive, intrusive, or require external apparatus such as breathalyzers, which the drinker may not possess. Speech is a viable modality for detecting intoxication from changes in vocal patterns. Intoxicated speech is slower, has lower amplitude, and is more prone to errors at the sentence, word, and phonological levels than sober speech. However, intoxication detection from speech is challenging due to high inter- and intra-user variability and the confounding effects of other factors such as fatigue, which may also impair speech. This paper investigates Wav2Vec 2.0, a self-supervised neural network architecture, for intoxication classification from audio. Wav2Vec 2.0 is a Transformer-based model that has demonstrated remarkable performance in various speech-related tasks. It analyzes raw audio directly by applying a multi-head attention mechanism to latent audio representations and was pre-trained on the Librispeech, Libri-Light and EmoDB datasets. The proposed model achieved an unweighted average recall of 73.3%, outperforming state-of-the-art models, highlighting its potential for accurate DUI detection to prevent alcohol-related incidents.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100562"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799304","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}

引用次数: 0