Artificial intelligence and machine learning in disorders of consciousness.

Autor:	Lee M; Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon, Republic of Korea., Laureys S; CERVO Brain Research Centre, Laval University, Québec, Canada.; Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.; Anesthesia, Critical Care and Pain Medicine Research, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, USA.; Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China.
Jazyk:	angličtina
Zdroj:	Current opinion in neurology [Curr Opin Neurol] 2024 Dec 01; Vol. 37 (6), pp. 614-620. Date of Electronic Publication: 2024 Oct 09.
DOI:	10.1097/WCO.0000000000001322
Abstrakt:	Purpose of Review: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions. Recent Findings: Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation). Summary: Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques. (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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