Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury
Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially har...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Neurotherapeutics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878747925000029 |
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| author | Zade Akras Jin Jing M. Brandon Westover Sahar F. Zafar |
| author_facet | Zade Akras Jin Jing M. Brandon Westover Sahar F. Zafar |
| author_sort | Zade Akras |
| collection | DOAJ |
| description | Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment. |
| format | Article |
| id | doaj-art-9c4aef9ff71c4b649fa2da4ec800d378 |
| institution | Kabale University |
| issn | 1878-7479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neurotherapeutics |
| spelling | doaj-art-9c4aef9ff71c4b649fa2da4ec800d3782025-02-01T04:11:55ZengElsevierNeurotherapeutics1878-74792025-01-01221e00524Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injuryZade Akras0Jin Jing1M. Brandon Westover2Sahar F. Zafar3Harvard Medical School, USADepartment of Neurology, Beth Israel Deaconess Medical Center, Boston MA, USADepartment of Neurology, Beth Israel Deaconess Medical Center, Boston MA, USADepartment of Neurology, Massachusetts General Hospital, Boston MA, USA; Corresponding author.Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.http://www.sciencedirect.com/science/article/pii/S1878747925000029Continuous EEGquantitative EEGartificial intelligencemachine learningseizuresneuroprognostication |
| spellingShingle | Zade Akras Jin Jing M. Brandon Westover Sahar F. Zafar Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury Neurotherapeutics Continuous EEG quantitative EEG artificial intelligence machine learning seizures neuroprognostication |
| title | Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury |
| title_full | Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury |
| title_fullStr | Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury |
| title_full_unstemmed | Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury |
| title_short | Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury |
| title_sort | using artificial intelligence to optimize anti seizure treatment and eeg guided decisions in severe brain injury |
| topic | Continuous EEG quantitative EEG artificial intelligence machine learning seizures neuroprognostication |
| url | http://www.sciencedirect.com/science/article/pii/S1878747925000029 |
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