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用于中风实时监测和早期检测的智能服装与医学成像创新:连接技术与患者护理

Smart Clothing and Medical Imaging Innovations for Real-Time Monitoring and Early Detection of Stroke: Bridging Technology and Patient Care.

作者信息

Sipos David, Vészi Kata, Bogár Bence, Pető Dániel, Füredi Gábor, Betlehem József, Pandur Attila András

机构信息

Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7400 Kaposvár, Hungary.

Dr. József Baka Diagnostic, Radiation Oncology, Research and Teaching Center, "Moritz Kaposi" Teaching Hospital, Guba Sándor Street 40, 7400 Kaposvár, Hungary.

出版信息

Diagnostics (Basel). 2025 Aug 6;15(15):1970. doi: 10.3390/diagnostics15151970.

Abstract

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis-enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)-is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach.

摘要

中风是一个重大的全球健康问题,其特征是脑血流突然中断,导致神经功能损害。通过计算机断层扫描(CT)和磁共振成像(MRI)等成像方式实现准确及时的诊断,对于区分中风类型以及启动溶栓、取栓或手术治疗等干预措施至关重要。与此同时,可穿戴技术,尤其是智能服装的最新进展,为中风预防、实时监测和康复提供了新机遇。这些服装集成了各种传感器,包括心电图(ECG)电极、脑电图(EEG)帽、肌电图(EMG)传感器以及运动或压力传感器,以持续跟踪生理和功能参数。例如,ECG衬衫监测心律以检测房颤,智能袜子评估步态不对称以早期发现活动能力下降,EEG帽在康复期间提供神经认知恢复的数据。这些技术支持中风全过程的个性化护理,从早期风险检测、急性事件监测到长期恢复。与人工智能驱动的分析相结合可进一步提高诊断准确性并优化治疗。本叙述性综述探讨了智能服装与传统成像相结合的应用,通过更积极主动、互联互通且以患者为中心的方法来改善中风管理和患者预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88a6/12345899/7d4554f7195a/diagnostics-15-01970-g001.jpg

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