The role of artificial intelligence in the rehabilitation of patients after stroke: A review



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Abstract

Stroke is a leading cause of disability and mortality worldwide, resulting from disruption of blood flow to the brain and leading to the development of significant neurological impairments that negatively impact patients' quality of life. AI technologies, including machine learning, convolutional neural networks, and brain-computer interfaces, make it possible to replicate the mechanisms of natural neural regeneration. AI-based rehabilitation systems can analyze individual patient characteristics and adapt therapeutic strategies in real time, analogous to the process of biological neuroplasticity in the brain. A search was conducted in the international and national electronic databases PubMed, Google Scholar, and eLibrary.ru. To formulate search queries, keywords and phrases reflecting key aspects of post-stroke rehabilitation using AI technologies were used: "artificial intelligence," "post-stroke rehabilitation," "stroke," "machine learning," "neurorehabilitation," "artificial intelligence," "stroke rehabilitation," "stroke," "neurorehabilitation," and "telemedicine." The integration of high-tech neuroimaging methods enhanced by AI algorithms has facilitated the modernization of diagnostics, particularly in the context of the use of deep learning technologies in the analysis of computed tomography and magnetic resonance imaging data, as well as for the automated identification of ischemic penumbras. Predictive modeling based on machine learning algorithms allows for the prediction of parameters such as the extent of functional recovery, the risk of complications, and the degree of disability. The integration of AI into the treatment of post-stroke patients raises a number of ethical, legal, and regulatory issues that must be addressed to ensure its effective use. AI is a tool with the potential to positively impact stroke rehabilitation, and its integration into the treatment process holds great promise. However, it faces a number of challenges that must be addressed to fully realize its potential. Despite challenges such as data heterogeneity and the need for interdisciplinary collaboration, advances in AI technology can contribute to improved stroke rehabilitation outcomes.

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About the authors

Tatyana B. Grinenko

First St. Petersburg State Medical University named after academician I.P. Pavlov, Saint Petersburg, Russian Federation

Author for correspondence.
Email: gr1nencko.tat@yandex.ru
ORCID iD: 0009-0004-5213-3689

resident

Russian Federation, 197022, Russian Federation, St. Petersburg, Leo Tolstoy St., 6-8

Zhanna D. Krasovskaya

First St. Petersburg State Medical University named after academician I.P. Pavlov, Saint Petersburg, Russian Federation

Email: jankinkra@gmail.com
ORCID iD: 0009-0003-3851-6766

Assistant

Russian Federation, 197022, Russian Federation, St. Petersburg, Leo Tolstoy St., 6-8

Anna A. Salimgarieva

Izhevsk State Medical Academy, Izhevsk, Russian Federation

Email: desenko@yandex.ru
ORCID iD: 0009-0000-5320-3168

Assistant

426034, Udmurt Republic, Izhevsk, st. Kommunarov, 281

Artem A. Filippov

Izhevsk State Medical Academy, Izhevsk, Russian Federation

Email: artem14090@yandex.ru
ORCID iD: 0009-0005-3830-8312

Resident

426034, Udmurt Republic, Izhevsk, st. Kommunarov, 281

Riyaz R. Khakimov

Izhevsk State Medical Academy, Izhevsk, Russian Federation

Email: piranya200@gmail.com
ORCID iD: 0009-0006-9480-668X

Resident

426034, Udmurt Republic, Izhevsk, st. Kommunarov, 281

Ildar I. Lutfarakhmanov

Bashkir State Medical University, Ufa, Russian Federation

Email: lutfarahmanov@yandex.ru
ORCID iD: 0000-0002-5829-5054
SPIN-code: 8047-1348

Professor, Doctor of Medical Sciences

Russian Federation, 450008, Republic of Bashkortostan, Ufa, st. Lenina, 3

Alfiza I. Kagarmanova

Bashkir State Medical University, Ufa, Russian Federation

Email: a.sun06072@gmail.com
ORCID iD: 0009-0000-5876-9603

Resedent

Russian Federation, 450008, Republic of Bashkortostan, Ufa, st. Lenina, 3

Aigul R. Faizullina

Bashkir State Medical University, Ufa, Russian Federation

Email: cwosl@mail.ru
ORCID iD: 0009-0008-5126-6024

Assistant

Russian Federation, 450008, Republic of Bashkortostan, Ufa, st. Lenina, 3

Azaliya E. Atlasova

Izhevsk State Medical Academy, Izhevsk, Russian Federation

Email: atlazalia1@gmail.com
ORCID iD: 0009-0003-7807-8203

Resident

Russian Federation, 426034, Udmurt Republic, Izhevsk, st. Kommunarov, 281

Liana S. Melokyan

V.I. Razumovsky Saratov State Medical University, Saratov, Russian Federation

Email: melokyanliana@mail.ru
ORCID iD: 0009-0000-0088-3139

Resident

Russian Federation, 410012, Volga Federal District, Saratov Region, Saratov, Bolshaya Kazachya St., 112

Ruslan A. Kurnosykh

N.N. Burdenko Voronezh State Medical University, Voronezh, Russian Federation

Email: bchemadicted@gmail.com
ORCID iD: 0009-0005-2377-9453

Reident

Russian Federation, 394036, Voronezh region, Voronezh, Studencheskaya street, 10

Ksenia O. Karimova

Bashkir State Medical University, Ufa, Russian Federation

Email: kstepanov1@yandex.ru
ORCID iD: 0009-0006-7048-0937

Student

450008, Republic of Bashkortostan, Ufa, st. Lenina, 3

Elvira P. Uryaeva

Kazan State Medical University, Kazan, Russian Federation

Email: uryeva@bk.ru
ORCID iD: 0009-0006-8960-7897

Student

Russian Federation, 420008, Volga Federal District, Republic of Tatarstan, Kazan, st. Universitetskaya, 13

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