{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:55:25Z","timestamp":1791482125231,"version":"4.3.4"},"reference-count":117,"publisher":"American Association for the Advancement of Science (AAAS)","content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Cyborg Bionic Syst"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>Artificial intelligence is being studied across the stroke care pathway, but evidence from acute diagnosis, early prognosis, rehabilitation-outcome prediction, and long-term management is often discussed without clearly separating these clinical tasks. This structured narrative review adds an integrated framework that maps each application to its decision point, relevance to rehabilitation, validation level, and readiness for clinical use. Unlike previous technology-centered reviews, it explicitly separates acute prognostic evidence from rehabilitation-specific evidence and distinguishes technical performance from transportability, clinical impact, and rehabilitation benefit. Acute imaging and prognostic models may provide baseline information for later rehabilitation planning, but their diagnostic or prognostic performance does not establish rehabilitation efficacy. Most rehabilitation models and robotic, virtual-reality, brain\u2013computer interface, wearable, and home-monitoring applications remain supported mainly by internal validation or early exploratory studies. Small or selected datasets, limited external and prospective validation, uncertain workflow effects, and sparse patient-centered outcomes continue to constrain clinical interpretation.<\/jats:p>","DOI":"10.34133\/cbsystems.0702","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T10:20:16Z","timestamp":1788430816000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Application of Artificial Intelligence in the Prediction and Management of Stroke Rehabilitation"],"prefix":"10.34133","volume":"7","author":[{"given":"Weihua","family":"Song","sequence":"first","affiliation":[{"name":"Department of Neurology, Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, \rCapital Medical University, Beijing 100053, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Si","sequence":"additional","affiliation":[{"name":"School of Medical Technology, Zhengzhou Academy of Intelligent Technology, \rBeijing Institute of Technology, Beijing 100081, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"Luohe Orthopedic Hospital","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, \rCapital Medical University, Beijing 100053, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Medical Technology, Zhengzhou Academy of Intelligent Technology, \rBeijing Institute of Technology, Beijing 100081, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"issue":"6","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"e98","DOI":"10.1161\/STR.0000000000000098","article-title":"Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association\/American Stroke Association","volume":"47","author":"Winstein CJ","year":"2016","unstructured":"Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC, Deruyter F, Eng JJ, Fisher B, Harvey RL, et al. Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association\/American Stroke Association. Stroke. 2016;47(6):e98\u2013e169.","journal-title":"Stroke"},{"issue":"4","key":"e_1_3_3_3_2","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1111\/ijs.12029","article-title":"Stroke rehabilitation in China: A systematic review and meta-analysis","volume":"9","author":"Zhang WW","year":"2014","unstructured":"Zhang WW, Speare S, Churilov L, Thuy M, Donnan G, Bernhardt J. Stroke rehabilitation in China: A systematic review and meta-analysis. Int J Stroke. 2014;9(4):494\u2013502.","journal-title":"Int J Stroke"},{"issue":"3","key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1161\/STROKEAHA.116.015501","article-title":"Stroke recovery and rehabilitation research: Issues, opportunities, and the National Institutes of Health StrokeNet","volume":"48","author":"Cramer SC","year":"2017","unstructured":"Cramer SC, Wolf SL, Adams HP Jr, Chen D, Dromerick AW, Dunning K, Ellerbe C, Grande A, Janis S, Lansberg MG, et al. Stroke recovery and rehabilitation research: Issues, opportunities, and the National Institutes of Health StrokeNet. Stroke. 2017;48(3):813\u2013819.","journal-title":"Stroke"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","first-page":"11024","DOI":"10.1109\/ACCESS.2023.3236084","article-title":"The role of artificial intelligence in future rehabilitation services: A systematic literature review","volume":"11","author":"Mennella C","year":"2023","unstructured":"Mennella C, Maniscalco U, De Pietro G, Esposito M. The role of artificial intelligence in future rehabilitation services: A systematic literature review. IEEE Access. 2023;11:11024\u201311043.","journal-title":"IEEE Access"},{"key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"52161","DOI":"10.1109\/ACCESS.2024.3383140","article-title":"Deep learning and artificial intelligence in action (2019\u20132023): A review on brain stroke detection, diagnosis, and intelligent post-stroke rehabilitation management","volume":"12","author":"Chaki J","year":"2024","unstructured":"Chaki J, Wo\u017aniak M. Deep learning and artificial intelligence in action (2019\u20132023): A review on brain stroke detection, diagnosis, and intelligent post-stroke rehabilitation management. IEEE Access. 2024;12:52161\u201352181.","journal-title":"IEEE Access"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3219085"},{"issue":"20","key":"e_1_3_3_8_2","doi-asserted-by":"crossref","first-page":"6585","DOI":"10.3390\/s24206585","article-title":"AI applications in adult stroke recovery and rehabilitation: A scoping review using AI","volume":"24","author":"Senadheera I","year":"2024","unstructured":"Senadheera I, Hettiarachchi P, Haslam B, Nawaratne R, Sheehan J, Lockwood KJ, Alahakoon D, Carey LM. AI applications in adult stroke recovery and rehabilitation: A scoping review using AI. Sensors. 2024;24(20):6585.","journal-title":"Sensors"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","DOI":"10.1177\/20552076251323833","article-title":"The application of artificial intelligence in stroke research: A bibliometric analysis","volume":"11","author":"Peng Y","year":"2025","unstructured":"Peng Y, Zhao Z, Rao Y, Sun K, Zou J, Liu G. The application of artificial intelligence in stroke research: A bibliometric analysis. Digit Health. 2025;11: Article 20552076251323833.","journal-title":"Digit Health"},{"issue":"11","key":"e_1_3_3_10_2","doi-asserted-by":"crossref","first-page":"3755","DOI":"10.3390\/jcm12113755","article-title":"Artificial intelligence and machine learning in the diagnosis and management of stroke: A narrative review of United States Food and Drug Administration-approved technologies","volume":"12","author":"Chandrabhatla AS","year":"2023","unstructured":"Chandrabhatla AS, Kuo EA, Sokolowski JD, Kellogg RT, Park M, Mastorakos P. Artificial intelligence and machine learning in the diagnosis and management of stroke: A narrative review of United States Food and Drug Administration-approved technologies. J Clin Med. 2023;12(11):3755.","journal-title":"J Clin Med"},{"issue":"6","key":"e_1_3_3_11_2","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1161\/STROKEAHA.123.043004","article-title":"Artificial intelligence in clinical decisions support for stroke: Balancing opportunity with caution","volume":"54","author":"Bravata DM","year":"2023","unstructured":"Bravata DM, Ranta A. Artificial intelligence in clinical decisions support for stroke: Balancing opportunity with caution. Stroke. 2023;54(6):1517\u20131518.","journal-title":"Stroke"},{"issue":"1","key":"e_1_3_3_12_2","article-title":"Abstract TP89: Advancing stroke care efficiency: Impact of AI and communication tools on patient outcomes","volume":"56","author":"Milfred F","year":"2025","unstructured":"Milfred F, Roy A, Delmor C, Bansal A, Gifford K, Ma K, et al. Abstract TP89: Advancing stroke care efficiency: Impact of AI and communication tools on patient outcomes. Stroke. 2025;56(Suppl 1): 10.1161\/str.56.suppl_1.tp89.","journal-title":"Stroke"},{"issue":"9778","key":"e_1_3_3_13_2","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1016\/S0140-6736(11)60325-5","article-title":"Stroke rehabilitation","volume":"377","author":"Langhorne P","year":"2011","unstructured":"Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. Lancet. 2011;377(9778):1693\u20131702.","journal-title":"Lancet"},{"issue":"2","key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/10749357.1994.11754021","article-title":"The effects of brain stem stroke on communication and swallowing","volume":"1","author":"Boyle M","year":"1994","unstructured":"Boyle M. The effects of brain stem stroke on communication and swallowing. Top Stroke Rehabil. 1994;1(2):76\u201386.","journal-title":"Top Stroke Rehabil"},{"key":"e_1_3_3_15_2","article-title":"Systemic inflammation in the recovery stage of stroke: Its association with sarcopenia and poor functional rehabilitation outcomes","volume":"3","author":"Yoshimura Y","year":"2018","unstructured":"Yoshimura Y, Bise T, Nagano F, Shimazu S, Shiraishi A, Yamaga M, Koga H. Systemic inflammation in the recovery stage of stroke: Its association with sarcopenia and poor functional rehabilitation outcomes. Prog Rehabil Med. 2018;3: Article 20180011.","journal-title":"Prog Rehabil Med"},{"issue":"1","key":"e_1_3_3_16_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s42466-020-00060-6","article-title":"Recovery from stroke: Current concepts and future perspectives","volume":"2","author":"Grefkes C","year":"2020","unstructured":"Grefkes C, Fink GR. Recovery from stroke: Current concepts and future perspectives. Neurol Res Pract. 2020;2(1):17.","journal-title":"Neurol Res Pract"},{"issue":"1","key":"e_1_3_3_17_2","article-title":"Virtual reality rehabilitation for stroke patients: Recent review and research issues","volume":"1905","author":"Arip ESM","year":"2017","unstructured":"Arip ESM, Ismail W, Nordin MJ, Radman A. Virtual reality rehabilitation for stroke patients: Recent review and research issues. AIP Conf Proc. 2017;1905(1): Article 050007.","journal-title":"AIP Conf Proc"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cmpb.2017.08.008","article-title":"Motion Rehab AVE 3D: A VR-based exergame for post-stroke rehabilitation","volume":"151","author":"Trombetta M","year":"2017","unstructured":"Trombetta M, Henrique PPB, Brum MR, Colussi EL, De Marchi ACB, Rieder R. Motion Rehab AVE 3D: A VR-based exergame for post-stroke rehabilitation. Comput Methods Prog Biomed. 2017;151:15\u201320.","journal-title":"Comput Methods Prog Biomed"},{"issue":"11","key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1177\/0269215514532851","article-title":"Virtual reality for upper extremity rehabilitation in early stroke: A pilot randomized controlled trial","volume":"28","author":"Yin CW","year":"2014","unstructured":"Yin CW, Sien NY, Ying LA, Chung SFCM, Leng TM. Virtual reality for upper extremity rehabilitation in early stroke: A pilot randomized controlled trial. Clin Rehabil. 2014;28(11):1107\u20131114.","journal-title":"Clin Rehabil"},{"issue":"1","key":"e_1_3_3_20_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12984-021-00804-8","article-title":"Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): A pilot study","volume":"18","author":"Keeling AB","year":"2021","unstructured":"Keeling AB, Piitz M, Semrau JA, Hill MD, Scott SH, Dukelow SP. Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): A pilot study. J Neuroeng Rehabil. 2021;18(1):10.","journal-title":"J Neuroeng Rehabil"},{"issue":"1","key":"e_1_3_3_21_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/1743-0003-8-33","article-title":"The development of an adaptive upper-limb stroke rehabilitation robotic system","volume":"8","author":"Kan P","year":"2011","unstructured":"Kan P, Huq R, Hoey J, Goetschalckx R, Mihailidis A. The development of an adaptive upper-limb stroke rehabilitation robotic system. J Neuroeng Rehabil. 2011;8(1):33.","journal-title":"J Neuroeng Rehabil"},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3389\/fbioe.2020.00111","article-title":"Soft rehabilitation actuator with integrated post-stroke finger spasticity evaluation","volume":"8","author":"Heung HL","year":"2020","unstructured":"Heung HL, Tang ZQ, Shi XQ, Tong KY, Li Z. Soft rehabilitation actuator with integrated post-stroke finger spasticity evaluation. Front Bioeng Biotechnol. 2020;8:111.","journal-title":"Front Bioeng Biotechnol"},{"key":"e_1_3_3_23_2","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.procir.2017.02.040","article-title":"An interactive upper limb rehab device for elderly stroke patients","volume":"60","author":"Jie S","year":"2017","unstructured":"Jie S, Haoyong Y, Chaw TL, Chiang CC, Vijayavenkataraman S. An interactive upper limb rehab device for elderly stroke patients. Procedia CIRP. 2017;60:488\u2013493.","journal-title":"Procedia CIRP"},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.3389\/fneur.2017.00447","article-title":"Early stroke rehabilitation of the upper limb assisted with an electromyography-driven neuromuscular electrical stimulation-robotic arm","volume":"8","author":"Qian Q","year":"2017","unstructured":"Qian Q, Hu X, Lai Q, Ng SC, Zheng Y, Poon W. Early stroke rehabilitation of the upper limb assisted with an electromyography-driven neuromuscular electrical stimulation-robotic arm. Front Neurol. 2017;8:447.","journal-title":"Front Neurol"},{"key":"e_1_3_3_25_2","doi-asserted-by":"crossref","first-page":"30283","DOI":"10.1109\/ACCESS.2021.3055960","article-title":"Upper limb rehabilitation system for stroke survivors based on multi-modal sensors and machine learning","volume":"9","author":"Miao S","year":"2021","unstructured":"Miao S, Shen C, Feng X, Zhu Q, Shorfuzzaman M, Lv Z. Upper limb rehabilitation system for stroke survivors based on multi-modal sensors and machine learning. IEEE Access. 2021;9:30283\u201330291.","journal-title":"IEEE Access"},{"issue":"1","key":"e_1_3_3_26_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s12984-015-0048-y","article-title":"The H2 robotic exoskeleton for gait rehabilitation after stroke: Early findings from a clinical study","volume":"12","author":"Bortole M","year":"2015","unstructured":"Bortole M, Venkatakrishnan A, Zhu F, Moreno JC, Francisco GE, Pons JL, Contreras-Vidal JL. The H2 robotic exoskeleton for gait rehabilitation after stroke: Early findings from a clinical study. J Neuroeng Rehabil. 2015;12(1):54.","journal-title":"J Neuroeng Rehabil"},{"issue":"3","key":"e_1_3_3_27_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.5573\/IEIESPC.2023.12.3.234","article-title":"AI-assisted physical therapy for post-injury rehabilitation: Current state of the art","volume":"12","author":"Ekambaram D","year":"2023","unstructured":"Ekambaram D, Ponnusamy V. AI-assisted physical therapy for post-injury rehabilitation: Current state of the art. IEIE Trans Smart Process Comput. 2023;12(3):234\u2013242.","journal-title":"IEIE Trans Smart Process Comput"},{"key":"e_1_3_3_28_2","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3389\/frobt.2018.00130","article-title":"High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training","volume":"5","author":"Irimia DC","year":"2018","unstructured":"Irimia DC, Ortner R, Poboroniuc MS, Ignat BE, Guger C. High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training. Front Robot AI. 2018;5:130.","journal-title":"Front Robot AI"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.2984003"},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","unstructured":"K\u00fcbler A Kleih S Mattia D. Brain computer interfaces for cognitive rehabilitation after stroke. In: Ib\u00e1\u00f1ez J Gonz\u00e1lez-Vargas J Azor\u00edn J Akay M Pons J editors. Converging clinical and engineering research on neurorehabilitation II. Cham (Switzerland): Springer; 2017. p. 847\u2013852.","DOI":"10.1007\/978-3-319-46669-9_138"},{"key":"e_1_3_3_31_2","unstructured":"Prokopowicz P Miko\u0142ajewski D Tyburek K Miko\u0142ajewska E Kotlarz P. AI-based analysis of selected gait parameters in post-stroke patients. In: Chora\u015b M Chora\u015b R editors. Image processing and communications challenges 11. Cham (Switzerland): Springer; 2020. p. 197\u2013205."},{"issue":"1","key":"e_1_3_3_32_2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1161\/STROKEAHA.111.627679","article-title":"Evidence-based quality indicators for stroke rehabilitation","volume":"43","author":"Grube MM","year":"2012","unstructured":"Grube MM, Dohle C, Djouchadar D, Rech P, Bienek K, Dietz-Fricke U, J\u00f6bges M, Kohler M, Missala I, Sch\u00f6nherr B, et al. Evidence-based quality indicators for stroke rehabilitation. Stroke. 2012;43(1):142\u2013146.","journal-title":"Stroke"},{"issue":"4","key":"e_1_3_3_33_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1002\/pnp.288","article-title":"The role of multidisciplinary team care in stroke rehabilitation","volume":"17","author":"Clarke DJ","year":"2013","unstructured":"Clarke DJ. The role of multidisciplinary team care in stroke rehabilitation. Prog Neurol Psychiatry. 2013;17(4):5\u20138.","journal-title":"Prog Neurol Psychiatry"},{"issue":"7","key":"e_1_3_3_34_2","article-title":"Occupational therapy for adults with problems in activities of daily living after stroke","volume":"7","author":"Legg LA","year":"2017","unstructured":"Legg LA, Lewis SR, Schofield-Robinson OJ, Drummond A, Langhorne P. Occupational therapy for adults with problems in activities of daily living after stroke. Cochrane Database Syst Rev. 2017;7(7): Article CD003585.","journal-title":"Cochrane Database Syst Rev"},{"issue":"3","key":"e_1_3_3_35_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s40141-014-0056-z","article-title":"Current trends in robot-assisted upper-limb stroke rehabilitation: Promoting patient engagement in therapy","volume":"2","author":"Blank AA","year":"2014","unstructured":"Blank AA, French JA, Pehlivan AU, O\u2019Malley MK. Current trends in robot-assisted upper-limb stroke rehabilitation: Promoting patient engagement in therapy. Curr Phys Med Rehabil Rep. 2014;2(3):184\u2013195.","journal-title":"Curr Phys Med Rehabil Rep"},{"key":"e_1_3_3_36_2","unstructured":"Gillen G Nilsen DM editors. Stroke rehabilitation: A function-based approach. 4th ed. St. Louis (MO): Elsevier; 2016."},{"key":"e_1_3_3_37_2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2023.1129516","article-title":"An optimal model of long-term post-stroke care","volume":"14","author":"Sarzy\u0144ska-D\u0142ugosz I","year":"2023","unstructured":"Sarzy\u0144ska-D\u0142ugosz I. An optimal model of long-term post-stroke care. Front Neurol. 2023;14: Article 1129516.","journal-title":"Front Neurol"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","first-page":"875","DOI":"10.3389\/fneur.2020.00875","article-title":"Consensus-based core set of outcome measures for clinical motor rehabilitation after stroke\u2014A Delphi study","volume":"11","author":"Pohl J","year":"2020","unstructured":"Pohl J, Held JPO, Verheyden G, Murphy MA, Engelter S, Fl\u00f6el A, Keller T, Kwakkel G, Nef T, Ward N, et al. Consensus-based core set of outcome measures for clinical motor rehabilitation after stroke\u2014A Delphi study. Front Neurol. 2020;11:875.","journal-title":"Front Neurol"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"issue":"4","key":"e_1_3_3_40_2","doi-asserted-by":"crossref","first-page":"769","DOI":"10.4103\/1673-5374.382228","article-title":"Machine learning applications in stroke medicine: Advancements, challenges, and future perspectives","volume":"19","author":"Daidone M","year":"2024","unstructured":"Daidone M, Ferrantelli S, Tuttolomondo A. Machine learning applications in stroke medicine: Advancements, challenges, and future perspectives. Neural Regen Res. 2024;19(4):769\u2013773.","journal-title":"Neural Regen Res"},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","unstructured":"Gupta R. AI-based technologies challenges and solutions for neurorehabilitation: A systematic mapping. In: Hemanth DJ editor. Computational intelligence and deep learning methods for neuro-rehabilitation applications. London (UK): Elsevier; 2024. p. 1\u201325.","DOI":"10.1016\/B978-0-443-13772-3.00014-5"},{"issue":"3","key":"e_1_3_3_42_2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5853\/jos.2017.02054","article-title":"Deep into the brain: Artificial intelligence in stroke imaging","volume":"19","author":"Lee E-J","year":"2017","unstructured":"Lee E-J, Kim Y-H, Kim N, Kang D-W. Deep into the brain: Artificial intelligence in stroke imaging. J Stroke. 2017;19(3):277\u2013285.","journal-title":"J Stroke"},{"issue":"4","key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1002\/ana.26435","article-title":"Deep learning applications for acute stroke management","volume":"92","author":"Chavva IR","year":"2022","unstructured":"Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, et al. Deep learning applications for acute stroke management. Ann Neurol. 2022;92(4):574\u2013587.","journal-title":"Ann Neurol"},{"issue":"1","key":"e_1_3_3_44_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s44230-023-00051-1","article-title":"Machine learning algorithms for the prediction of language and cognition rehabilitation outcomes of post-stroke patients: A scoping review","volume":"4","author":"Apostolidis K","year":"2023","unstructured":"Apostolidis K, Kokkotis C, Moustakidis S, Karakasis E, Sakellari P, Koutra C, Tsiptsios D, Karatzetzou S, Vadikolias K, Aggelousis N. Machine learning algorithms for the prediction of language and cognition rehabilitation outcomes of post-stroke patients: A scoping review. Hum Centric Intell Syst. 2023;4(1):147\u2013160.","journal-title":"Hum Centric Intell Syst"},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1256592","article-title":"The predictive performance of artificial intelligence on the outcome of stroke: A systematic review and meta-analysis","volume":"17","author":"Yang Y","year":"2023","unstructured":"Yang Y, Tang L, Deng Y, Li X, Luo A, Zhang Z, He L, Zhu C, Zhou M. The predictive performance of artificial intelligence on the outcome of stroke: A systematic review and meta-analysis. Front Neurosci. 2023;17: Article 1256592.","journal-title":"Front Neurosci"},{"issue":"13","key":"e_1_3_3_46_2","doi-asserted-by":"crossref","first-page":"4355","DOI":"10.3390\/s24134355","article-title":"Comprehensive review: Machine and deep learning in brain stroke diagnosis","volume":"24","author":"Fernandes J","year":"2024","unstructured":"Fernandes J, Cardoso VEM, Comesa\u00f1a-Campos A, Pinheira A. Comprehensive review: Machine and deep learning in brain stroke diagnosis. Sensors. 2024;24(13):4355.","journal-title":"Sensors"},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106835","article-title":"Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review","volume":"158","author":"Sardari S","year":"2023","unstructured":"Sardari S, Sharifzadeh S, Daneshkhah A, Nakisa B, Loke SW, Palade V, Duncan MJ. Artificial intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Comput Biol Med. 2023;158: Article 106835.","journal-title":"Comput Biol Med"},{"issue":"1","key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s12984-022-01032-4","article-title":"Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: A systematic review","volume":"19","author":"Campagnini S","year":"2022","unstructured":"Campagnini S, Arienti C, Patrini M, Liuzzi P, Mannini A, Carrozza MC. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: A systematic review. J Neuroeng Rehabil. 2022;19(1):54.","journal-title":"J Neuroeng Rehabil"},{"key":"e_1_3_3_49_2","doi-asserted-by":"crossref","DOI":"10.1007\/s13748-024-00357-6","article-title":"Explainable AI-driven decision support system for personalizing rehabilitation routines in stroke recovery","author":"Mart\u00ednez-Cid S","year":"2025","unstructured":"Mart\u00ednez-Cid S, Vallejo D, Herrera V, Schez-Sobrino S, Castro-Schez JJ, Albusac JA. Explainable AI-driven decision support system for personalizing rehabilitation routines in stroke recovery. Prog Artif Intell. 2025; 10.1007\/s13748-024-00357-6.","journal-title":"Prog Artif Intell"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0250"},{"issue":"3","key":"e_1_3_3_51_2","doi-asserted-by":"crossref","DOI":"10.2196\/32508","article-title":"Machine learning models for predicting influential factors of early outcomes in acute ischemic stroke: Registry-based study","volume":"10","author":"Su P-Y","year":"2022","unstructured":"Su P-Y, Wei Y-C, Luo H, Liu C-H, Huang W-Y, Chen K-F, Lin C-P, Wei H-Y, Lee T-H. Machine learning models for predicting influential factors of early outcomes in acute ischemic stroke: Registry-based study. JMIR Med Inform. 2022;10(3): Article e32508.","journal-title":"JMIR Med Inform"},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","unstructured":"Zihni E McGarry B Kelleher J. Moving toward explainable decisions of artificial intelligence models for the prediction of functional outcomes of ischemic stroke patients. In: Linwood SL editor. Digital health. Brisbane (Australia): Exon Publications; 2022. p. 73\u201390.","DOI":"10.36255\/exon-publications-digital-health-explainable-decisions"},{"key":"e_1_3_3_53_2","unstructured":"Lee MH. Interactive hybrid intelligence systems for human-AI\/robot collaboration: Improving the practices of physical stroke rehabilitation [dissertation]. [Pittsburgh (PA)]: Carnegie Mellon University; 2021."},{"key":"e_1_3_3_54_2","doi-asserted-by":"crossref","unstructured":"Lu Z Qi T. Discussion on AI-based interactive system of cerebral stroke rehabilitation system. In: Abawajy J Choo KK Xu Z Atiquzzaman M editors. 2020 international conference on applications and techniques in cyber intelligence. Cham (Switzerland): Springer; 2021. p. 320\u2013327.","DOI":"10.1007\/978-3-030-53980-1_48"},{"key":"e_1_3_3_55_2","doi-asserted-by":"crossref","unstructured":"Lee MH Siewiorek DP Smailagic A Bernardino A Berm\u00fadez i Badia S. Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment. In: CHIL \u201920: Proceedings of the ACM conference on health inference and learning. New York (NY): Association for Computing Machinery; 2020. p. 160\u2013169.","DOI":"10.1145\/3368555.3384452"},{"key":"e_1_3_3_56_2","doi-asserted-by":"crossref","unstructured":"Lee MH. An intelligent decision support system for stroke rehabilitation assessment. In: ASSETS \u201919: Proceedings of the 21st international ACM SIGACCESS conference on computers and accessibility. New York (NY): Association for Computing Machinery; 2019. p. 694\u2013696.","DOI":"10.1145\/3308561.3356106"},{"key":"e_1_3_3_57_2","doi-asserted-by":"crossref","unstructured":"Lee MH Siewiorek DP Smailagic A Bernardino A Badia SBI. Learning to assess the quality of stroke rehabilitation exercises. In: IUI \u201919: Proceedings of the 24th international conference on intelligent user interfaces. New York (NY): Association for Computing Machinery; 2019. p. 218\u2013228.","DOI":"10.1145\/3301275.3302273"},{"issue":"24","key":"e_1_3_3_58_2","doi-asserted-by":"crossref","first-page":"9859","DOI":"10.3390\/s22249859","article-title":"Explainable artificial intelligence model for stroke prediction using EEG signal","volume":"22","author":"Islam MS","year":"2022","unstructured":"Islam MS, Hussain I, Rahman MM, Park SJ, Hossain MA. Explainable artificial intelligence model for stroke prediction using EEG signal. Sensors. 2022;22(24):9859.","journal-title":"Sensors"},{"issue":"19","key":"e_1_3_3_59_2","doi-asserted-by":"crossref","first-page":"6791","DOI":"10.3390\/app10196791","article-title":"AI-based stroke disease prediction system using real-time electromyography signals","volume":"10","author":"Yu J","year":"2020","unstructured":"Yu J, Park S, Kwon SH, Ho CMB, Pyo CS, Lee H. AI-based stroke disease prediction system using real-time electromyography signals. Appl Sci. 2020;10(19):6791.","journal-title":"Appl Sci"},{"issue":"5","key":"e_1_3_3_60_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/10749357.2021.1926149","article-title":"Use of artificial intelligence as an instrument of evaluation after stroke: A scoping review based on international classification of functioning, disability and health concept: AI applications for stroke evaluation","volume":"29","author":"Luvizutto GJ","year":"2022","unstructured":"Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, de Moura NE, de Souza JT, Wincker FC, Miranda LA, Hamamoto Filho PT, et al. Use of artificial intelligence as an instrument of evaluation after stroke: A scoping review based on international classification of functioning, disability and health concept: AI applications for stroke evaluation. Top Stroke Rehabil. 2022;29(5):331\u2013346.","journal-title":"Top Stroke Rehabil"},{"issue":"6","key":"e_1_3_3_61_2","doi-asserted-by":"crossref","first-page":"8867","DOI":"10.1007\/s11227-021-04209-1","article-title":"AI-based stroke prediction system using body motion biosignals during walking","volume":"78","author":"Yu J","year":"2022","unstructured":"Yu J, Park S, Ho CMB, Kwon S-H, Cho K-H, Lee YS. AI-based stroke prediction system using body motion biosignals during walking. J Supercomput. 2022;78(6):8867\u20138889.","journal-title":"J Supercomput"},{"key":"e_1_3_3_62_2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2021.734345","article-title":"Machine learning in action: Stroke diagnosis and outcome prediction","volume":"12","author":"Mainali S","year":"2021","unstructured":"Mainali S, Darsie ME, Smetana KS. Machine learning in action: Stroke diagnosis and outcome prediction. Front Neurol. 2021;12: Article 734345.","journal-title":"Front Neurol"},{"key":"e_1_3_3_63_2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2024.1367854","article-title":"A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke","volume":"15","author":"Zhao Z","year":"2024","unstructured":"Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol. 2024;15: Article 1367854.","journal-title":"Front Neurol"},{"key":"e_1_3_3_64_2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2024.1394879","article-title":"Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network","volume":"15","author":"Yang Y","year":"2024","unstructured":"Yang Y, Guo Y. Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network. Front Neurol. 2024;15: Article 1394879.","journal-title":"Front Neurol"},{"issue":"1","key":"e_1_3_3_65_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s12984-022-01075-7","article-title":"Cross-validation of predictive models for functional recovery after post-stroke rehabilitation","volume":"19","author":"Campagnini S","year":"2022","unstructured":"Campagnini S, Liuzzi P, Mannini A, Basagni B, Macchi C, Carrozza MC, Cecchi F. Cross-validation of predictive models for functional recovery after post-stroke rehabilitation. J Neuroeng Rehabil. 2022;19(1):96.","journal-title":"J Neuroeng Rehabil"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00328-w"},{"issue":"1","key":"e_1_3_3_67_2","doi-asserted-by":"crossref","first-page":"38136","DOI":"10.1038\/s41598-025-21945-z","article-title":"A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata","volume":"15","author":"Amador K","year":"2025","unstructured":"Amador K, Winder AJ, Fiehler J, Barber PA, Wilms M, Forkert ND. A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata. Sci Rep. 2025;15(1):38136.","journal-title":"Sci Rep"},{"key":"e_1_3_3_68_2","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2022.884693","article-title":"Interpretable machine learning modeling for ischemic stroke outcome prediction","volume":"13","author":"Jabal MS","year":"2022","unstructured":"Jabal MS, Joly O, Kallmes DF, Harston G, Rabinstein AA, Huynh T, Brinjikji W. Interpretable machine learning modeling for ischemic stroke outcome prediction. Front Neurol. 2022;13: Article 884693.","journal-title":"Front Neurol"},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.nicl.2024.103638","article-title":"Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI","volume":"43","author":"White A","year":"2024","unstructured":"White A, Saranti M, Garcez AD, Hope TMH, Price CJ, Bowman H. Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI. Neuroimage Clin. 2024;43: Article 103638.","journal-title":"Neuroimage Clin"},{"issue":"1","key":"e_1_3_3_70_2","doi-asserted-by":"crossref","first-page":"20127","DOI":"10.1038\/s41598-020-77243-3","article-title":"Machine learning to predict mortality after rehabilitation among patients with severe stroke","volume":"10","author":"Scrutinio D","year":"2020","unstructured":"Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D\u2019Addio G. Machine learning to predict mortality after rehabilitation among patients with severe stroke. Sci Rep. 2020;10(1):20127.","journal-title":"Sci Rep"},{"issue":"8","key":"e_1_3_3_71_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/02699052.2025.2472188","article-title":"Enhancing ischemic stroke management: Leveraging machine learning models for predicting patient recovery after alteplase treatment","volume":"39","author":"Khorsand B","year":"2025","unstructured":"Khorsand B, Vaghf A, Salimi V, Zand M, Ghoreishi SA. Enhancing ischemic stroke management: Leveraging machine learning models for predicting patient recovery after alteplase treatment. Brain Inj. 2025;39(8):671\u2013677.","journal-title":"Brain Inj"},{"issue":"4","key":"e_1_3_3_72_2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.neurad.2021.05.001","article-title":"Leveraging artificial intelligence in ischemic stroke imaging","volume":"49","author":"Shafaat O","year":"2022","unstructured":"Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, Sotoudeh E, Sair HI, Yousem DM, Sotoudeh H. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol. 2022;49(4):343\u2013351.","journal-title":"J Neuroradiol"},{"issue":"6","key":"e_1_3_3_73_2","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3390\/life11060488","article-title":"How to improve the management of acute ischemic stroke by modern technologies, artificial intelligence, and new treatment methods","volume":"11","author":"Zele\u0148\u00e1k K","year":"2021","unstructured":"Zele\u0148\u00e1k K, Krajina A, Meyer L, Fiehler J, ESMINT Artificial Intelligence and Robotics Ad hoc Committee, Behme D, Bulja D, Caroff J, Chotai AA, Da Ros V, et al. How to improve the management of acute ischemic stroke by modern technologies, artificial intelligence, and new treatment methods. Life. 2021;11(6):488.","journal-title":"Life"},{"issue":"1","key":"e_1_3_3_74_2","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s11357-024-01301-1","article-title":"From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke","volume":"47","author":"Cacciotti A","year":"2025","unstructured":"Cacciotti A, Pappalettera C, Miraglia F, Carrarini C, Pecchioli C, Rossini PM, Vecchio F. From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke. GeroScience. 2025;47(1):977\u2013992.","journal-title":"GeroScience"},{"key":"e_1_3_3_75_2","doi-asserted-by":"crossref","unstructured":"Halloran S Tang L Guan Y Shi JQ Eyre J. Remote monitoring of stroke patients\u2019 rehabilitation using wearable accelerometers. In: Proceedings of the 23rd international symposium on wearable computers. New York (NY): Association for Computing Machinery; 2019. p. 72\u201377. doi: 10.1145\/3341163.3347731","DOI":"10.1145\/3341163.3347731"},{"issue":"1","key":"e_1_3_3_76_2","article-title":"Medical Internet of Things to realize elderly stroke prevention and nursing management","volume":"2021","author":"Li X","year":"2021","unstructured":"Li X, Ren S, Gu F. Medical Internet of Things to realize elderly stroke prevention and nursing management. J Healthc Eng. 2021;2021(1): Article 9989602.","journal-title":"J Healthc Eng"},{"issue":"24","key":"e_1_3_3_77_2","doi-asserted-by":"crossref","first-page":"11806","DOI":"10.3390\/app142411806","article-title":"The potential of artificial intelligence in predicting post-stroke rehabilitation outcomes: Statistical analysis considering Rivermead Motor Assessment and activities of daily living indicators and selected demographic variables","volume":"14","author":"Ku\u017anar M","year":"2024","unstructured":"Ku\u017anar M, Lorenc A. The potential of artificial intelligence in predicting post-stroke rehabilitation outcomes: Statistical analysis considering Rivermead Motor Assessment and activities of daily living indicators and selected demographic variables. Appl Sci. 2024;14(24):11806.","journal-title":"Appl Sci"},{"issue":"1","key":"e_1_3_3_78_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s12984-023-01151-6","article-title":"Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: A machine learning study","volume":"20","author":"Chen Y-W","year":"2023","unstructured":"Chen Y-W, Lin K-C, Li Y-C, Lin C-J. Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: A machine learning study. J Neuroeng Rehabil. 2023;20(1):25.","journal-title":"J Neuroeng Rehabil"},{"issue":"1","key":"e_1_3_3_79_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/JBHI.2022.3220179","article-title":"eXplainable AI allows predicting upper limb rehabilitation outcomes in sub-acute stroke patients","volume":"27","author":"Gandolfi M","year":"2023","unstructured":"Gandolfi M, Boscolo Galazzo I, Gasparin Pavan R, Cruciani F, Val\u00e8 N, Picelli A, Storti SF, Smania N, Menegaz G. eXplainable AI allows predicting upper limb rehabilitation outcomes in sub-acute stroke patients. IEEE J Biomed Health Inform. 2023;27(1):263\u2013273.","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_3_3_80_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2023.111081","article-title":"An artificial intelligence-based prognostic prediction model for hemorrhagic stroke","volume":"167","author":"Chen Y","year":"2023","unstructured":"Chen Y, Jiang C, Chang J, Qin C, Zhang Q, Ye Z, Li Z, Tian F, Ma W, Feng M, et al. An artificial intelligence-based prognostic prediction model for hemorrhagic stroke. Eur J Radiol. 2023;167: Article 111081.","journal-title":"Eur J Radiol"},{"issue":"1","key":"e_1_3_3_81_2","doi-asserted-by":"crossref","first-page":"11235","DOI":"10.1038\/s41598-022-14986-1","article-title":"Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke","volume":"12","author":"Liao W-W","year":"2022","unstructured":"Liao W-W, Hsieh Y-W, Lee T-H, Chen C-L, Wu C-Y. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Sci Rep. 2022;12(1):11235.","journal-title":"Sci Rep"},{"key":"e_1_3_3_82_2","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/5489084","article-title":"An artificial intelligence-based bio-medical stroke prediction and analytical system using a machine learning approach","volume":"2022","author":"Pitchai R","year":"2022","unstructured":"Pitchai R, Dappuri B, Pramila PV, Vidhyalakshmi M, Shanthi S, Alonazi WB, Almutairi KMA, Sundaram RS, Beyene I. An artificial intelligence-based bio-medical stroke prediction and analytical system using a machine learning approach. Comput Intell Neurosci. 2022;2022: Article 5489084.","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"e_1_3_3_83_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.4103\/jsip.jsip_44_24","article-title":"Evaluating the efficacy of artificial intelligence using machine learning models in predicting functional recovery post-stroke","volume":"9","author":"Deole M","year":"2025","unstructured":"Deole M, Dharwadkar R. Evaluating the efficacy of artificial intelligence using machine learning models in predicting functional recovery post-stroke. J Soc Indian Physiother. 2025;9(1):41\u201346.","journal-title":"J Soc Indian Physiother"},{"issue":"12","key":"e_1_3_3_84_2","doi-asserted-by":"crossref","first-page":"916","DOI":"10.7326\/M19-1695","article-title":"The management of stroke rehabilitation: A synopsis of the 2019 US Department of Veterans Affairs and US Department of Defense clinical practice guideline","volume":"171","author":"Sall J","year":"2019","unstructured":"Sall J, Eapen BC, Tran JE, Bowles AO, Bursaw A, Rodgers ME. The management of stroke rehabilitation: A synopsis of the 2019 US Department of Veterans Affairs and US Department of Defense clinical practice guideline. Ann Intern Med. 2019;171(12):916\u2013924.","journal-title":"Ann Intern Med"},{"key":"e_1_3_3_85_2","doi-asserted-by":"crossref","unstructured":"Vourvopoulos A Marin-Pardo O Neureither M Saldana D Jahng E Liew SL. Multimodal head-mounted virtual-reality brain-computer interface for stroke rehabilitation. In: Chen JYC Fragomeni G editors. Virtual augmented and mixed reality: Multimodal interaction. Cham (Switzerland): Springer; 2019. p. 165\u2013179.","DOI":"10.1007\/978-3-030-21607-8_13"},{"key":"e_1_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0248"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0379"},{"issue":"1","key":"e_1_3_3_88_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1055\/a-1508-7980","article-title":"A new paradigm of \u201creal-time\u201d stroke risk prediction and integrated care management in the digital health era: Innovations using machine learning and artificial intelligence approaches","volume":"122","author":"Guo Y","year":"2022","unstructured":"Guo Y. A new paradigm of \u201creal-time\u201d stroke risk prediction and integrated care management in the digital health era: Innovations using machine learning and artificial intelligence approaches. Thromb Haemost. 2022;122(1):5\u20137.","journal-title":"Thromb Haemost"},{"issue":"3","key":"e_1_3_3_89_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/10400435.2019.1593260","article-title":"Assistive game controller for artificial intelligence-enhanced telerehabilitation post-stroke","volume":"33","author":"Burdea G","year":"2021","unstructured":"Burdea G, Kim N, Polistico K, Kadaru A, Grampurohit N, Roll D, Damiani F. Assistive game controller for artificial intelligence-enhanced telerehabilitation post-stroke. Assist Technol. 2021;33(3):117\u2013128.","journal-title":"Assist Technol"},{"issue":"11","key":"e_1_3_3_90_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0225150","article-title":"Effectiveness of telerehabilitation in the management of adults with stroke: A systematic review","volume":"14","author":"Appleby E","year":"2019","unstructured":"Appleby E, Gill ST, Hayes LK, Walker TL, Walsh M, Kumar S. Effectiveness of telerehabilitation in the management of adults with stroke: A systematic review. PLOS ONE. 2019;14(11): Article e0225150.","journal-title":"PLOS ONE"},{"issue":"1","key":"e_1_3_3_91_2","doi-asserted-by":"crossref","DOI":"10.2196\/rehab.5079","article-title":"A personalized self-management rehabilitation system with an intelligent shoe for stroke survivors: A realist evaluation","volume":"3","author":"Mawson S","year":"2016","unstructured":"Mawson S, Nasr N, Parker J, Davies R, Zheng H, Mountain G. A personalized self-management rehabilitation system with an intelligent shoe for stroke survivors: A realist evaluation. JMIR Rehabil Assist Technol. 2016;3(1): Article e1.","journal-title":"JMIR Rehabil Assist Technol"},{"key":"e_1_3_3_92_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.dajour.2023.100218","article-title":"An artificial intelligence model for smart post-stroke assessment using wearable sensors","volume":"7","author":"Razfar N","year":"2023","unstructured":"Razfar N, Kashef R, Mohammadi F. An artificial intelligence model for smart post-stroke assessment using wearable sensors. Decis Anal J. 2023;7: Article 100218.","journal-title":"Decis Anal J"},{"key":"e_1_3_3_93_2","doi-asserted-by":"crossref","unstructured":"Chen YC Yang CJ. Deep learning real-time detection and correction system for stroke rehabilitation posture. In: 2021 international conference on technologies and applications of artificial intelligence (TAAI). Piscataway (NJ): IEEE; 2021. p. 254\u2013259.","DOI":"10.1109\/TAAI54685.2021.00056"},{"issue":"3","key":"e_1_3_3_94_2","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s00146-019-00925-8","article-title":"Hand rehabilitation assessment system using leap motion controller","volume":"35","author":"Weiss Cohen M","year":"2020","unstructured":"Weiss Cohen M, Regazzoni D. Hand rehabilitation assessment system using leap motion controller. AI Soc. 2020;35(3):581\u2013594.","journal-title":"AI Soc"},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0195"},{"issue":"6","key":"e_1_3_3_96_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0287308","article-title":"Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review","volume":"18","author":"Zu W","year":"2023","unstructured":"Zu W, Huang X, Xu T, Du L, Wang Y, Wang L, Nie W. Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review. PLOS ONE. 2023;18(6): Article e0287308.","journal-title":"PLOS ONE"},{"issue":"3","key":"e_1_3_3_97_2","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1007\/s13311-023-01358-4","article-title":"Big data in stroke: How to use big data to make the next management decision","volume":"20","author":"Liu Y","year":"2023","unstructured":"Liu Y, Luo Y, Naidech AM. Big data in stroke: How to use big data to make the next management decision. Neurotherapeutics. 2023;20(3):744\u2013757.","journal-title":"Neurotherapeutics"},{"key":"e_1_3_3_98_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.11336\/jjcrs.15.1","article-title":"Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward?","volume":"15","author":"Ono K","year":"2024","unstructured":"Ono K, Takahashi R, Morita K, Ara Y, Abe S, Ito S, Uno S, Abe M, Shirasaka T. Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward? Jpn J Compr Rehabil Sci. 2024;15:1\u20137.","journal-title":"Jpn J Compr Rehabil Sci"},{"key":"e_1_3_3_99_2","doi-asserted-by":"crossref","unstructured":"Amann J. Machine learning in stroke medicine: opportunities and challenges for risk prediction and prevention. In: Friedrich O Wolkenstein A editors. Advances in neuroethics. Cham (Switzerland): Springer; 2021. p. 57\u201371.","DOI":"10.1007\/978-3-030-74188-4_5"},{"key":"e_1_3_3_100_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.smhl.2021.100239","article-title":"A progressive prediction model towards home-based stroke rehabilitation programs","volume":"23","author":"Bo W","year":"2022","unstructured":"Bo W, Cavuoto L, Langan J, Subryan H, Bhattacharjya S, Huang MC, Xu W. A progressive prediction model towards home-based stroke rehabilitation programs. Smart Health. 2022;23: Article 100239.","journal-title":"Smart Health"},{"issue":"7","key":"e_1_3_3_101_2","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1161\/STROKEAHA.121.036557","article-title":"Prediction of recurrent ischemic stroke using registry data and machine learning methods: The Erlangen Stroke Registry","volume":"53","author":"Vodencarevic A","year":"2022","unstructured":"Vodencarevic A, Weing\u00e4rtner M, Caro JJ, Ukalovic D, Zimmermann-Rittereiser M, Schwab S, Kolominsky-Rabas P. Prediction of recurrent ischemic stroke using registry data and machine learning methods: The Erlangen Stroke Registry. Stroke. 2022;53(7):2299\u20132306.","journal-title":"Stroke"},{"issue":"2","key":"e_1_3_3_102_2","article-title":"Use of artificial intelligence to enhance case management and job development practices in rehabilitation counseling","volume":"31","author":"Skerritt C","year":"2023","unstructured":"Skerritt C, Wolstein D. Use of artificial intelligence to enhance case management and job development practices in rehabilitation counseling. Rehabil Prof. 2023;31(2).","journal-title":"Rehabil Prof"},{"key":"e_1_3_3_103_2","doi-asserted-by":"crossref","unstructured":"Lee MH Siewiorek DP Smailagic A Bernardino A Berm\u00fadez i Badia S. Towards efficient annotations for a human-AI collaborative clinical decision support system: A case study on physical stroke rehabilitation assessment. In: IUI \u201922: Proceedings of the 27th international conference on intelligent user interfaces. New York (NY): Association for Computing Machinery; 2022. p. 4\u201314.","DOI":"10.1145\/3490099.3511112"},{"issue":"1","key":"e_1_3_3_104_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41582-019-0287-9","article-title":"Towards artificial intelligence for clinical stroke care","volume":"16","author":"Leslie-Mazwi TM","year":"2020","unstructured":"Leslie-Mazwi TM, Lev MH. Towards artificial intelligence for clinical stroke care. Nat Rev Neurol. 2020;16(1):5\u20136.","journal-title":"Nat Rev Neurol"},{"issue":"10","key":"e_1_3_3_105_2","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.3390\/life13102061","article-title":"Stroke recovery is a journey: Prediction and potentials of motor recovery after a stroke from a practical perspective","volume":"13","author":"Li S","year":"2023","unstructured":"Li S. Stroke recovery is a journey: Prediction and potentials of motor recovery after a stroke from a practical perspective. Life. 2023;13(10):2061.","journal-title":"Life"},{"issue":"10","key":"e_1_3_3_106_2","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.3390\/biomedicines12102415","article-title":"Towards transforming neurorehabilitation: The impact of artificial intelligence on diagnosis and treatment of neurological disorders","volume":"12","author":"Bonanno M","year":"2024","unstructured":"Bonanno M, Quartarone A, Mojdehdehbaher S, Celesti A, Calabr\u00f2 RS. Towards transforming neurorehabilitation: The impact of artificial intelligence on diagnosis and treatment of neurological disorders. Biomedicines. 2024;12(10):2415.","journal-title":"Biomedicines"},{"issue":"14","key":"e_1_3_3_107_2","doi-asserted-by":"crossref","first-page":"6347","DOI":"10.3390\/app14146347","article-title":"A comprehensive review on brain\u2013computer interface (BCI)-based machine and deep learning algorithms for stroke rehabilitation","volume":"14","author":"Elashmawi WH","year":"2024","unstructured":"Elashmawi WH, Ayman A, Antoun M, Mohamed H, Mohamed SE, Amr H, Talaat Y, Ali A. A comprehensive review on brain\u2013computer interface (BCI)-based machine and deep learning algorithms for stroke rehabilitation. Appl Sci. 2024;14(14):6347.","journal-title":"Appl Sci"},{"issue":"6","key":"e_1_3_3_108_2","doi-asserted-by":"crossref","first-page":"521","DOI":"10.3109\/17483107.2013.840863","article-title":"Developing a personalised self-management system for post stroke rehabilitation; utilising a user-centred design methodology","volume":"9","author":"Mawson S","year":"2014","unstructured":"Mawson S, Nasr N, Parker J, Zheng H, Davies R, Mountain G. Developing a personalised self-management system for post stroke rehabilitation; utilising a user-centred design methodology. Disabil Rehabil Assist Technol. 2014;9(6):521\u2013528.","journal-title":"Disabil Rehabil Assist Technol"},{"issue":"1","key":"e_1_3_3_109_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10796-025-10580-8","article-title":"Extended reality and artificial intelligence in medicine and rehabilitation","volume":"27","author":"Krilavi\u010dius T","year":"2025","unstructured":"Krilavi\u010dius T, De Paolis LT, De Luca V, Spjut J. Extended reality and artificial intelligence in medicine and rehabilitation. Inf Syst Front. 2025;27(1):1\u20136.","journal-title":"Inf Syst Front"},{"key":"e_1_3_3_110_2","doi-asserted-by":"crossref","unstructured":"Das A Day TW Kulkarni V Buchanan A Cottrell K John NW Chatterjee K. Towards intelligent extended reality in stroke rehabilitation. In: Pillai AS Menon B editors. Augmenting neurological disorder prediction and rehabilitation using artificial intelligence. London (UK): Academic Press; 2022. p. 309\u2013329.","DOI":"10.1016\/B978-0-323-90037-9.00006-0"},{"key":"e_1_3_3_111_2","doi-asserted-by":"publisher","DOI":"10.34133\/cbsystems.0293"},{"key":"e_1_3_3_112_2","doi-asserted-by":"crossref","first-page":"983","DOI":"10.3389\/fphys.2018.00983","article-title":"Tai Chi for stroke rehabilitation: A systematic review and meta-analysis of randomized controlled trials","volume":"9","author":"Lyu D","year":"2018","unstructured":"Lyu D, Lyu X, Zhang Y, Ren Y, Yang F, Zhou L, Zou Y, Li Z. Tai Chi for stroke rehabilitation: A systematic review and meta-analysis of randomized controlled trials. Front Physiol. 2018;9:983.","journal-title":"Front Physiol"},{"issue":"1","key":"e_1_3_3_113_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1186\/1745-6215-15-484","article-title":"Applying Tai Chi as a rehabilitation program for stroke patients in the recovery phase: Study protocol for a randomized controlled trial","volume":"15","author":"Zhang Y","year":"2014","unstructured":"Zhang Y, Liu H, Zhou L, Chen K, Jin H, Zou Y, Li Z. Applying Tai Chi as a rehabilitation program for stroke patients in the recovery phase: Study protocol for a randomized controlled trial. Trials. 2014;15(1):484.","journal-title":"Trials"},{"issue":"8","key":"e_1_3_3_114_2","doi-asserted-by":"crossref","DOI":"10.1161\/STROKEAHA.110.582718","article-title":"Acupuncture during stroke rehabilitation","volume":"41","author":"Ernst E","year":"2010","unstructured":"Ernst E, Lee MS. Acupuncture during stroke rehabilitation. Stroke. 2010;41(8): Article e549.","journal-title":"Stroke"},{"issue":"6","key":"e_1_3_3_115_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.3390\/e19060242","article-title":"A framework for designing the architectures of deep convolutional neural networks","volume":"19","author":"Albelwi S","year":"2017","unstructured":"Albelwi S, Mahmood A. A framework for designing the architectures of deep convolutional neural networks. Entropy. 2017;19(6):242.","journal-title":"Entropy"},{"issue":"6","key":"e_1_3_3_116_2","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/rs12060939","article-title":"A multi-level attention model for remote sensing image captions","volume":"12","author":"Li Y","year":"2020","unstructured":"Li Y, Fang S, Jiao L, Liu R, Shang R. A multi-level attention model for remote sensing image captions. Remote Sens. 2020;12(6):939.","journal-title":"Remote Sens"},{"issue":"10","key":"e_1_3_3_117_2","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.3390\/app14104316","article-title":"A historical survey of advances in transformer architectures","volume":"14","author":"Sajun AR","year":"2024","unstructured":"Sajun AR, Zualkernan I, Sankalpa D. A historical survey of advances in transformer architectures. Appl Sci. 2024;14(10):4316.","journal-title":"Appl Sci"},{"issue":"1","key":"e_1_3_3_118_2","doi-asserted-by":"crossref","first-page":"11178","DOI":"10.1038\/s41598-022-15374-5","article-title":"RF-CNN-F: Random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance","volume":"12","author":"Khozeimeh F","year":"2022","unstructured":"Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Tartibi M, Hussain S, Sani ZA, Khodatars M, et al. RF-CNN-F: Random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Sci Rep. 2022;12(1):11178.","journal-title":"Sci Rep"}],"container-title":["Cyborg and Bionic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.34133\/cbsystems.0702","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:25:18Z","timestamp":1791480318000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.34133\/cbsystems.0702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":117,"alternative-id":["10.34133\/cbsystems.0702"],"URL":"https:\/\/doi.org\/10.34133\/cbsystems.0702","relation":{},"ISSN":["2692-7632"],"issn-type":[{"value":"2692-7632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"assertion":[{"value":"2026-07-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-27","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"0702"}}