A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1367854/full |
| _version_ | 1827304512989167616 |
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| author | Zijian Zhao Yuanyuan Zhang Jiuhui Su Lianbo Yang Luhang Pang Yingshan Gao Hongbo Wang |
| author_facet | Zijian Zhao Yuanyuan Zhang Jiuhui Su Lianbo Yang Luhang Pang Yingshan Gao Hongbo Wang |
| author_sort | Zijian Zhao |
| collection | DOAJ |
| description | Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke. |
| first_indexed | 2024-04-24T17:38:12Z |
| format | Article |
| id | doaj.art-1e1352bac6e948c8b6d91529ab28dc98 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-24T17:38:12Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-1e1352bac6e948c8b6d91529ab28dc982024-03-28T04:25:46ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-03-011510.3389/fneur.2024.13678541367854A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic strokeZijian Zhao0Yuanyuan Zhang1Jiuhui Su2Lianbo Yang3Luhang Pang4Yingshan Gao5Hongbo Wang6Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, ChinaRehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, ChinaDepartment of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, ChinaDepartment of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, ChinaDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, ChinaDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, ChinaDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, ChinaStroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.https://www.frontiersin.org/articles/10.3389/fneur.2024.1367854/fullischemic strokerehabilitationartificial intelligenceMRICT |
| spellingShingle | Zijian Zhao Yuanyuan Zhang Jiuhui Su Lianbo Yang Luhang Pang Yingshan Gao Hongbo Wang A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke Frontiers in Neurology ischemic stroke rehabilitation artificial intelligence MRI CT |
| title | A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| title_full | A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| title_fullStr | A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| title_full_unstemmed | A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| title_short | A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| title_sort | comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke |
| topic | ischemic stroke rehabilitation artificial intelligence MRI CT |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1367854/full |
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