Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare ...Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare delivery[1,2].Among diverse advanced AI technologies,an intelligent agent with multi-parameter perception,decision-making,and execution capabilities demonstrates the potential for facilitating the development of next-generation optoelectronic devices.The intelligent agent is a physical or abstract entity that acts autonomously,perceives and interacts with its environment,and communicates with other agents[3].It could perceive dynamic environmental conditions,execute actions,and make appropriate decisions.Fabric emerges as an ideal carrier for human-centered intelligent agents,providing various properties such as perceptibility,adaptability,and wearability.Intelligent fabric,known for its unique functionality,has attracted considerable attention from academia and industry.In 2014,Germany proposed a national strategy called FutureTEX to upgrade the entire textile industry by promoting integration between textiles and other fields.Two years later,the United States announced the establishment of the Revolutionary Fibers and Textiles Manufacturing Innovation Institute,which intends to accelerate the revival of fabric manufacturing.Compared with conventional fibers,revolutionary fibers focus on the design of multiple materials and structures,enabling the integration of various functionalities into a single fiber.Particularly in the United States,the advent of the digital revolution,advancements in Internet of Things technology,and mature fiber technology significantly boost the development of the intelligent fiber industry.Notable commercial applications of intelligent fibers are gradually emerging.Project Jacquard,a collaborative effort by Google and Levi’s,presents an intelligent jacket that combines the washability and texture of standard fabrics with the interactive functionalities of electronic products.Apple Inc.has developed intelligent garments,accessories,and household items with capabilities to“read”physiological indicators such as weight,body temperature,and sedentary duration on sofas.展开更多
Atrial fibrillation(AF)is the most common supraventricular arrhythmia in clinical practice,and many patients exhibit silent AF.Variables based on Electrocardiogram(ECG)have shown promise in assessing AF risk in the pr...Atrial fibrillation(AF)is the most common supraventricular arrhythmia in clinical practice,and many patients exhibit silent AF.Variables based on Electrocardiogram(ECG)have shown promise in assessing AF risk in the previous study.This study protocol proposes a systematic approach,named RAF-ECP,to evaluate the role of ECG phenotypes in assessing the risk of AF.The protocol aims to standardize the definition and calculation of ECG phenotypes,ensuring consistency and compa-rability across different research studies and healthcare settings.Data will be collected from multiple clinical laboratories,with an anticipated sample size of 10,000 cases(lead I and II,10 s)evenly distributed between subjects with and without AF events in one-year time frame.By analyzing ECG data and baseline information,statistical tests and machine learning classifiers will be employed to identify significant risk factors and develop a comprehensive risk assessment model for AF.The anticipated outcomes include hazard ratio values,confidence intervals,p values,as well as accuracy,sensitivity,and specificity measures.The study also discusses the clinical relevance and potential benefits of standardizing ECG phenotypes,emphasizing the need for collaboration between multiple centers to obtain diverse and representative datasets.The proposed RAF-ECP study protocol offers a novel and significant approach to understanding the impact of ECG phenotypes on AF risk assessment.Its integration of statistical analysis and machine learning techniques has the potential to advance AF research and contribute to the development of improved risk prediction models and clinical decision support tools.展开更多
基金supported by the National Natural Science Foundation of China(T2425018 and 62175082 to Guangming Tao,and 62371138 to Cuiwei Yang)the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ039 to Guangming Tao)+3 种基金the National Key Research and Development Program of China(2022YFB3805800 to Chong Hou)The Open Project Program of Wuhan National Laboratory for Optoelectronics(2023083 to Ning Zhou)Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences(Sports Industry Research Center of Huazhong University of Science and Technology)China Postdoctoral Science Foundation(2023M731184 to Maiping Yang).
文摘Artificial intelligence(AI)is crucial in driving scientific,technological,and industrial advancements,and it has given rise to an ambient intelligence that can potentially improve the physical execution of healthcare delivery[1,2].Among diverse advanced AI technologies,an intelligent agent with multi-parameter perception,decision-making,and execution capabilities demonstrates the potential for facilitating the development of next-generation optoelectronic devices.The intelligent agent is a physical or abstract entity that acts autonomously,perceives and interacts with its environment,and communicates with other agents[3].It could perceive dynamic environmental conditions,execute actions,and make appropriate decisions.Fabric emerges as an ideal carrier for human-centered intelligent agents,providing various properties such as perceptibility,adaptability,and wearability.Intelligent fabric,known for its unique functionality,has attracted considerable attention from academia and industry.In 2014,Germany proposed a national strategy called FutureTEX to upgrade the entire textile industry by promoting integration between textiles and other fields.Two years later,the United States announced the establishment of the Revolutionary Fibers and Textiles Manufacturing Innovation Institute,which intends to accelerate the revival of fabric manufacturing.Compared with conventional fibers,revolutionary fibers focus on the design of multiple materials and structures,enabling the integration of various functionalities into a single fiber.Particularly in the United States,the advent of the digital revolution,advancements in Internet of Things technology,and mature fiber technology significantly boost the development of the intelligent fiber industry.Notable commercial applications of intelligent fibers are gradually emerging.Project Jacquard,a collaborative effort by Google and Levi’s,presents an intelligent jacket that combines the washability and texture of standard fabrics with the interactive functionalities of electronic products.Apple Inc.has developed intelligent garments,accessories,and household items with capabilities to“read”physiological indicators such as weight,body temperature,and sedentary duration on sofas.
基金funded by Shanghai Municipal Science and Technology Major Project,under Grant No.2017SHZDZX01Medical Scientific Research Key Project of Jiangsu Commission of Health,under Grant No.ZDB2020025.
文摘Atrial fibrillation(AF)is the most common supraventricular arrhythmia in clinical practice,and many patients exhibit silent AF.Variables based on Electrocardiogram(ECG)have shown promise in assessing AF risk in the previous study.This study protocol proposes a systematic approach,named RAF-ECP,to evaluate the role of ECG phenotypes in assessing the risk of AF.The protocol aims to standardize the definition and calculation of ECG phenotypes,ensuring consistency and compa-rability across different research studies and healthcare settings.Data will be collected from multiple clinical laboratories,with an anticipated sample size of 10,000 cases(lead I and II,10 s)evenly distributed between subjects with and without AF events in one-year time frame.By analyzing ECG data and baseline information,statistical tests and machine learning classifiers will be employed to identify significant risk factors and develop a comprehensive risk assessment model for AF.The anticipated outcomes include hazard ratio values,confidence intervals,p values,as well as accuracy,sensitivity,and specificity measures.The study also discusses the clinical relevance and potential benefits of standardizing ECG phenotypes,emphasizing the need for collaboration between multiple centers to obtain diverse and representative datasets.The proposed RAF-ECP study protocol offers a novel and significant approach to understanding the impact of ECG phenotypes on AF risk assessment.Its integration of statistical analysis and machine learning techniques has the potential to advance AF research and contribute to the development of improved risk prediction models and clinical decision support tools.
