Publication Name	World Journal of Critical Care Medicine
Manuscript ID	91397
Country	United States

Received	2023-12-27 20:59
Peer-Review Started	2023-12-27 20:59
To Make the First Decision
Return for Revision	2024-01-16 08:04
Revised	2024-01-27 16:27
Second Decision	2024-03-06 02:49
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-03-06 07:31
Articles in Press	2024-03-06 07:31
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-03-12 21:30
Typeset the Manuscript	2024-05-13 09:39
Publish the Manuscript Online	2024-06-03 10:23

ISSN	2220-3141(online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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Category	Clinical Neurology
Manuscript Type	Review
Article Title	Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning
Manuscript Source	Invited Manuscript
All Author List	Bahadar S Srichawla
ORCID	Author(s) ORCID Number Bahadar S Srichawla http://orcid.org/0000-0002-5301-4102
Funding Agency and Grant Number
Corresponding Author	Bahadar S Srichawla, Consultant, MS, Staff Physician, Department of Neurology, University of Massachusetts Chan Medical School, No. 55 Lake Ave N , Worcester, MA 01655, United States. bahadar.srichawla@umassmemorial.org
Key Words	Neurocritical care; Critical care; Multimodal monitoring; Machine learning; Neurophysics; Cerebral hemodynamics; Cerebral energetics; Transcranial doppler; Cerebral microdialysis; Near-infrared spectroscopy
Core Tip	This manuscript provides a comprehensive review of multimodal monitoring (MMM) in the intensive care unit (ICU), emphasizing the integration of neurophysics to optimize patient outcomes. It covers both invasive and non-invasive neuromonitoring tools. This review highlights the role of machine learning in real-time data analysis and interpretation from MMM tools, aiding in precise clinical decision-making. By integrating diverse data streams through MMM, machine learning algorithms enhance the understanding of cerebral physiology and disease, offering invaluable insights for personalized patient care in the ICU. This integration aids the neurointensivist in not only more accurate neuroprognostication, but also future avenues for targeted therapeutic interventions.
Publish Date	2024-06-03 10:23
Citation	<p>Srichawla BS. Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning. <i>World J Crit Care Med</i> 2024; 13(2): 91397</p>
URL	https://www.wjgnet.com/2220-3141/full/v13/i2/91397.htm
DOI	https://dx.doi.org/10.5492/wjccm.v13.i2.91397

Full Article (PDF)	WJCCM-13-91397-with-cover.pdf
Manuscript File	91397-Review-FilipodiaCL.docx
Answering Reviewers	91397-Answering reviewers.pdf
Audio Core Tip	91397-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	91397-Conflict-of-interest statement.pdf
Copyright License Agreement	91397-Copyright license agreement.pdf
Peer-review Report	91397-Peer-review(s).pdf
Scientific Editor Work List	91397-Scientific editor work list.pdf
CrossCheck Report	91397-CrossCheck report.pdf