中国卒中杂志 ›› 2025, Vol. 20 ›› Issue (8): 935-941.DOI: 10.3969/j.issn.1673-5765.2025.08.001
周宏宇,李子孝,王春娟
收稿日期:
2025-04-16
修回日期:
2025-07-19
接受日期:
2025-07-25
出版日期:
2025-08-20
发布日期:
2025-08-20
通讯作者:
王春娟 wangchunjuan@ncrcnd.org.cn
基金资助:
ZHOU Hongyu, LI Zixiao, WANG Chunjuan
Received:
2025-04-16
Revised:
2025-07-19
Accepted:
2025-07-25
Online:
2025-08-20
Published:
2025-08-20
Contact:
WANG Chunjuan, E-mail: wangchunjuan@ncrcnd.org.cn
摘要: 人工智能与终端设备的深度融合正推动着神经系统疾病全病程管理向智能化、连续化和精准化转型。本文系统阐述了智能手机、可穿戴设备、环境智能与机器人等终端设备在疾病全病程管理中的应用,提出以“监测-分析-决策-干预-反馈”为核心的闭环管理框架,并强调构建“医院-社区-家庭”一体化服务模式,为建立神经系统疾病全病程闭环管理新范式提供理论支持与实践路径。
中图分类号:
周宏宇, 李子孝, 王春娟. 人工智能赋能终端设备驱动的神经系统疾病全病程闭环管理[J]. 中国卒中杂志, 2025, 20(8): 935-941.
ZHOU Hongyu, LI Zixiao, WANG Chunjuan. Full-Course Closed-Loop Management for Neurological Disorders through Artificial Intelligence-Powered Terminal Devices[J]. Chinese Journal of Stroke, 2025, 20(8): 935-941.
[1] GBD 2021 Nervous System Disorders Collaborators. Global,regional,and national burden of disorders affecting the nervous system,1990-2021:a systematic analysis for the global burden of disease study 2021[J]. Lancet Neurol,2024,23(4):344-381. [2] ERICKSON C M,WEXLER A,LARGENT E A. Digital biomarkers for neurodegenerative disease[J]. JAMA Neurol,2025,82(1):5-6. [3] RICOTTI V,KADIRVELU B,SELBY V,et al. Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy[J]. Nat Med,2023,29(1):95-103. [4] KADIRVELU B,GAVRIEL C,NAGESHWARAN S,et al. A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia[J]. Nat Med,2023,29(1):86-94. [5] DONNER E,DEVINSKY O,FRIEDMAN D. Wearable digital health technology for epilepsy[J]. N Engl J Med,2024,390(8):736-745. [6] CHEN C R,DING S C,WANG J. Digital health for aging populations[J]. Nat Med,2023,29(7):1623-1630. [7] BUTLER P M,YANG J,BROWN R,et al. Smartwatch and smartphone-based remote assessment of brain health and detection of mild cognitive impairment[J]. Nat Med,2025,31(3):829-839. [8] CAI T A,NI H M,YU M L,et al. DeepStroke:an efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning[J/OL]. Med Image Anal,2022,80:102522[2025-04-10]. https://doi.org/10.1016/j.media.2022.102522. [9] NEVLER N,CHO S,COUSINS K A Q,et al. Changes in digital speech measures in asymptomatic carriers of pathogenic variants associated with frontotemporal degeneration[J/OL]. Neurology,2024,102(2):e207926[2025-04-10]. https://doi.org/10.1212/wnl.0000000000207926. [10] JEONG S M,SONG Y D,SEOK C L,et al. Machine learning-based classification of Parkinson’s disease using acoustic features:insights from multilingual speech tasks[J/OL]. Comput Biol Med,2024,182:109078[2025-04-10]. https://doi.org/10.1016/j.compbiomed.2024.109078. [11] SÁNCHEZ-SÁNCHEZ M L,RUESCAS-NICOLAU M A,ARNAL-GÓMEZ A,et al. Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis:a cross-sectional study[J/OL]. J Neuroeng Rehabil,2024,21(1):54[2025-04-10]. https://doi.org/10.1186/s12984-024-01346-5. [12] SCHÖNHERR C,ZIEGLER J,ZENTEK T,et al. Smartphone-based gait analysis in the assessment of fatigue and fatigability in people with multiple sclerosis:a supervised cohort study[J/OL]. J Neurol,2025,272(3):217[2025-07-10]. https://doi.org/10.1007/s00415-025-12906-7. [13] VELUVALI A,DEHGHANI ZAHEDANI A,HOSSEINIAN A,et al. Impact of digital health interventions on glycemic control and weight management[J/OL]. NPJ Digit Med,2025,8(1):20[2025-07-10]. https://doi.org/10.1038/s41746-025-01430-7. [14] KÓKAI L L,Ó CEALLAIGH D,WIJTZES A I,et al. App-based physical activity intervention among women with prior hypertensive pregnancy disorder:a randomized clinical trial[J/OL]. JAMA Netw Open,2025,8(4):e252656[2025-07-10]. https://doi.org/10.1001/JAMAnetworkopen.2025.2656. [15] BEDI S,LIU Y,ORR-EWING L,et al. Testing and evaluation of health care applications of large language models:a systematic review[J]. JAMA,2025,333(4):319-328. [16] RAO S,LI Y K,MAMOUEI M,et al. Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model[J/OL]. Lancet Digit Health,2025,7(6):100873[2025-07-10]. https://doi.org/10.1016/j.landig.2025.03.005. [17] ATES H C,NGUYEN P Q,GONZALEZ-MACIA L,et al. End-to-end design of wearable sensors[J]. Nat Rev Mater,2022,7(11):887-907. [18] ZHOU S,PARK G,LONGARDNER K,et al. Clinical validation of a wearable ultrasound sensor of blood pressure[J]. Nat Biomed Eng,2025,9(6):865-881. [19] HUGHES M S,ADDALA A,BUCKINGHAM B. Digital technology for diabetes[J]. N Engl J Med,2023,389(22):2076-2086. [20] SPATZ E S,GINSBURG G S,RUMSFELD J S,et al. Wearable digital health technologies for monitoring in cardiovascular medicine[J]. N Engl J Med,2024,390(4):346-356. [21] LOHSE K R,MILLER A E,BLAND M D,et al. Association between real-world actigraphy and poststroke motor recovery[J]. Stroke,2025,56(8):2079-2090. [22] SCHALKAMP A K,PEALL K J,HARRISON N A,et al. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis[J]. Nat Med,2023,29(8):2048-2056. [23] ZHAO H,CHEN W C,LI Y H,et al. In situ structural-functional synchronous dissection of dynamic neuromuscular system via an integrated multimodal wearable patch[J/OL]. Sci Adv,2025,11(2):eads1486[2025-07-10]. https://doi.org/10.1126/sciadv.ads1486. [24] GILL S K,BARSKY A,GUAN X,et al. Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers:the RATE-AF randomized trial[J]. Nat Med,2024,30(7):2030-2036. [25] SHAH K,WANG A R,CHEN Y W,et al. Automated loss of pulse detection on a consumer smartwatch[J]. Nature,2025,642(8066):174-181. [26] SINGH B,CHASTIN S,MIATKE A,et al. Real-world accuracy of wearable activity trackers for detecting medical conditions:systematic review and meta-analysis[J/OL]. JMIR Mhealth Uhealth,2024,12:e56972[2025-04-10]. https://doi.org/10.2196/56972. [27] OEHRN C R,CERNERA S,HAMMER L H,et al. Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease:a blinded randomized feasibility trial[J]. Nat Med,2024,30(11):3345-3356. [28] FRIEDRICH M U,RELTON S,WONG D,et al. Computer vision in clinical neurology:a review[J]. JAMA Neurol,2025,82(4):407-415. [29] YANG Y Z,YUAN Y,ZHANG G,et al. Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals[J]. Nat Med,2022,28(10):2207-2215. [30] HAQUE A,MILSTEIN A,FEI-FEI L. Illuminating the dark spaces of healthcare with ambient intelligence[J]. Nature,2020,585(7824):193-202. [31] ZHANG Y,LIU X Y,QIAO X F,et al. Characteristics and emerging trends in research on rehabilitation robots from 2001 to 2020:bibliometric study[J/OL]. J Med Internet Res,2023,25:e42901[2025-04-10]. https://doi.org/10.2196/42901. [32] KILGARD M P,EPPERSON J D,ADEHUNOLUWA E A,et al. Closed-loop vagus nerve stimulation aids recovery from spinal cord injury[J]. Nature,2025,643(8073):1030-1036. [33] XIA H S,ZHANG Y C,RAJABI N,et al. Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement[J/OL]. Nat Commun,2024,15(1):1760[2025-07-10]. https://doi.org/10.1038/s41467-024-46249-0. [34] HANKOV N,CABAN M,DEMESMAEKER R,et al. Augmenting rehabilitation robotics with spinal cord neuromodulation:a proof of concept[J/OL]. Sci Robot,2025,10(100):eadn5564[2025-07-10]. https://doi.org/10.1126/scirobotics.adn5564. [35] SHEN J,YU J H,ZHANG H,et al. Artificial intelligence-powered social robots for promoting physical activity in older adults:a systematic review[J/OL]. J Sport Health Sci,2025,14:101045[2025-07-10]. https://doi.org/10.1016/j.jshs.2025.101045. [36] DOSSO J A,BANDARI E,MALHOTRA A,et al. Towards emotionally aligned social robots for dementia:perspectives of care partners and persons with dementia[J/OL]. Alzheimers Dement,2022,18 Suppl 2:e059261[2025-04-10]. https://doi.org/10.1002/alz.059261. [37] CHEN S W,FAN S C,QIAO Z,et al. Transforming healthcare:intelligent wearable sensors empowered by smart materials and artificial intelligence[J/OL]. Adv Mater,2025,37(21):e2500412[2025-07-10]. https://doi.org/10.1002/adma.202500412. [38] PHIPPS J,PASSAGE B,SEL K,et al. Early adverse physiological event detection using commercial wearables:challenges and opportunities[J/OL]. NPJ Digit Med,2024,7(1):136[2025-04-10]. https://doi.org/10.1038/s41746-024-01129-1. [39] XIAO X,YIN J Y,XU J,et al. Advances in machine learning for wearable sensors[J]. ACS Nano,2024,18(34):22734-22751. [40] LIU J J,BORSARI B,LI Y,et al. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations[J]. Cell,2025,188(2):515-529. [41] THANGARAJ P M,BENSON S H,OIKONOMOU E K,et al. Cardiovascular care with digital twin technology in the era of generative artificial intelligence[J]. Eur Heart J,2024,45(45):4808-4821. [42] ZHANG W S,LING Y,CHEN Z L,et al. Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes[J/OL]. NPJ Digit Med,2024(7):169[2025-04-10]. https://doi.org/10.1038/s41746-024-01163-z. [43] YU B S,KAKU A,LIU K N,et al. Quantifying impairment and disease severity using AI models trained on healthy subjects[J/OL]. NPJ Digit Med,2024(7):180[2025-04-10]. https://doi.org/10.1038/s41746-024-01163-z. [44] LEE M,YEO N Y,AHN H J,et al. Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning[J/OL]. Alzheimers Res Ther,2023,15(1):147[2025-04-10]. https://doi.org/10.1186/s13195-023-01289-4. [45] STEVELINK R,AL-TOMA D,JANSEN F E,et al. Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy:a systematic review and individual participant data meta-analysis[J/OL]. EClinicalMedicine,2022,53:101732[2025-04-10]. https://doi.org/10.1016/j.eclinm.2022.101732. [46] TAN J,GONG E Y,GALLIS J A,et al. Primary care-based digital health-enabled stroke management intervention:long-term follow-up of a cluster randomized clinical trial[J/OL]. JAMA Netw Open,2024,7(12):e2449561[2025-04-10]. https://doi.org/10.1001/jamanetworkopen.2024.49561. [47] DHAMIJA R K,SALUJA A,GARG D,et al. Teleneurorehabilitation and motor and nonmotor symptoms and quality of life in Parkinson disease:the TELEPARK randomized clinical trial[J]. JAMA Neurol,2025,82(4):376-383. [48] BISWAS M,SABA L,OMERZU T,et al. A review on joint carotid intima-media thickness and plaque area measurement in ultrasound for cardiovascular/stroke risk monitoring:artificial intelligence framework[J]. J Digit Imaging,2021,34(3):581-604. [49] CHEN E,PRAKASH S,JANAPA REDDI V,et al. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring[J]. Nat Biomed Eng,2025,9(4):445-454. [50] GUTERUD M,FAGERHEIM BUGGE H,RØISLIEN J,et al. Prehospital screening of acute stroke with the National Institutes of Health stroke scale(ParaNASPP):a stepped-wedge,cluster-randomised controlled trial[J]. Lancet Neurol,2023,22(9):800-811. [51] CUI F F,HE X Y,ZHAI Y K,et al. Application of telemedicine services based on a regional telemedicine platform in China from 2014 to 2020:longitudinal trend analysis[J/OL]. J Med Internet Res,2021,23(7):e28009[2025-04-10]. https://doi.org/10.2196/28009. [52] SHENG Y Y,BOND R,JAISWAL R,et al. Augmenting K-means clustering with qualitative data to discover the engagement patterns of older adults with multimorbidity when using digital health technologies:proof-of-concept trial[J/OL]. J Med Internet Res,2024,26:e46287[2025-04-10]. https://doi.org/10.2196/46287. [53] GHASSEMI M,OAKDEN-RAYNER L,BEAM A L. The false hope of current approaches to explainable artificial intelligence in health care[J/OL]. Lancet Digit Health,2021,3(11):e745-e750[2025-04-10]. https://doi.org/10.1016/s2589-7500 (21) 00208-9. [54] GINSBURG G S,PICARD R W,FRIEND S H. Key issues as wearable digital health technologies enter clinical care[J]. N Engl J Med,2024,390(12):1118-1127. [55] BAYOUMY K,GABER M,ELSHAFEEY A,et al. Smart wearable devices in cardiovascular care:where we are and how to move forward[J]. Nat Rev Cardiol,2021,18(8):581-599. [56] ZINZUWADIA A,SINGH J P. Wearable devices-addressing bias and inequity[J/OL]. Lancet Digit Health,2022,4(12):e856-e857[2025-04-10]. https://doi.org/10.1016/s2589-7500 (22) 00194-7. [57] HERNANDEZ-BOUSSARD T,LEE A Y,STOYANOVICH J,et al. Promoting transparency in AI for biomedical and behavioral research[J]. Nat Med,2025,31(6):1733-1734. [58] SADÉE C,TESTA S,BARBA T,et al. Medical digital twins:enabling precision medicine and medical artificial intelligence[J/OL]. Lancet Digit Health,2025:100864[2025-07-10]. https://doi.org/10.1016/j.landig.2025.02.004. [59] 马锐华. 从头再来 揭开脑卒中患者出院后管理的秘密[M]. 北京:科学技术文献出版社,2025:249-255. MA R H. Starting anew:unveiling the secrets of post-discharge management for stroke patients[M]. Beijing:Scientific and Technical Documentation Press,2025:249-255. [60] CHEN R J,WANG J J,WILLIAMSON D F K,et al. Algorithmic fairness in artificial intelligence for medicine and healthcare[J]. Nat Biomed Eng,2023,7(6):719-742. [61] WILLIAMS C Y K,SUBRAMANIAN C R,ALI S S,et al. Physician- and large language model-generated hospital discharge summaries[J]. JAMA Intern Med,2025,185(7):818-825. [62] TU T,SCHAEKERMANN M,PALEPU A,et al. Towards conversational diagnostic artificial intelligence[J]. Nature,2025,642(8067):442-450. |
[1] | 熊俞婷, 王春娟. 人工智能在神经系统疾病中的应用进展[J]. 中国卒中杂志, 2025, 20(8): 950-957. |
[2] | 刘嘉, 刘雪梅. 泛凋亡与中枢神经系统疾病的研究进展[J]. 中国卒中杂志, 2025, 20(8): 1058-1065. |
[3] | 姜华, 张春芳, 陈晨, 刘飞凤, 李刚. 中国卒中院前急救现状及研究进展[J]. 中国卒中杂志, 2025, 20(7): 803-808. |
[4] | 勾岚, 姜明慧, 姜勇, 廖晓凌, 李昊, 张杰, 程丝. 人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战[J]. 中国卒中杂志, 2025, 20(6): 710-717. |
[5] | 周宏宇, 李子孝. 数字生物标志物:打开数智医疗应用的钥匙[J]. 中国卒中杂志, 2025, 20(4): 385-390. |
[6] | 池琦, 赵颖, 韩东倩, 张锶琪, 杜沛洁, 董琬玥, 徐安定, 杨振国, 孟珩. 急性缺血性卒中发病时间评估的研究进展[J]. 中国卒中杂志, 2025, 20(4): 493-499. |
[7] | 张淋源, 沈翔, 吕海燕, 王国栋, 吴云成. 人工智能在提升神经科住院医师脑血管病诊治能力中的应用与挑战[J]. 中国卒中杂志, 2025, 20(3): 380-383. |
[8] | 邳靖陶, 尹伟, 宋晓微, 武海岩, 梁怡凡, 高策舒, 魏宸铭, 桑振华, 安志明, 江燕敏, 吴雅婷, 牟婧宇, 赵心怡, 陈乐, 武剑. 不同年资神经内科医师对数智化诊疗的认知、态度及功能需求现状调查[J]. 中国卒中杂志, 2025, 20(1): 78-86. |
[9] | 吴春艳, 尹雅诗, 王广志, 岳奎涛. 急性缺血性卒中不同时间窗影像学评价及应用进展[J]. 中国卒中杂志, 2024, 19(9): 1094-1101. |
[10] | 谢雪微, 荆京, 姜倩梅, 索阅, 王义槐, 王拥军. 低场强磁共振成像系统的发展简史及其在神经系统疾病中的应用和展望 [J]. 中国卒中杂志, 2024, 19(7): 740-745. |
[11] | 孟令涉, 王春娟. 人工智能与机器学习在心脑血管疾病管理中的应用与前景:美国心脏学会使用人工智能改善心脏疾病结局科学声明解读[J]. 中国卒中杂志, 2024, 19(6): 621-631. |
[12] | 吴松笛. 关注视网膜血管成像分析,提升对神经系统疾病的全面认识[J]. 中国卒中杂志, 2024, 19(11): 1239-1245. |
[13] | 杨溢, 刘清源, 刘伟奇, 王硕. 基于人工智能的未破裂颅内动脉瘤形态学指标三维测量方法[J]. 中国卒中杂志, 2024, 19(1): 112-119. |
[14] | 张心邈, 徐曼, 丁玲玲, 荆京, 龚浠平, 董可辉, 赵性泉, 王拥军, 李子孝. 脑血管病临床决策支持系统对卒中医疗服务质量关键绩效指标的影响研究[J]. 中国卒中杂志, 2024, 19(1): 120-124. |
[15] | 戴昰旭, 张长青, 王展, 冯涛. 中枢神经系统表面铁沉积症1例报道[J]. 中国卒中杂志, 2023, 18(03): 335-339. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||