Noninvasive continuous blood pressure prediction using FlexNIRS and machine learning during carotid endarterectomy
This preliminary study supports the feasibility of noninvasive MAP estimation using NIRS-PPG and machine learning. The approach may provide a practical alternative for BP monitoring after A-line removal in postoperative and intensive care unit settings.
Authors
- Alyssa Martin (ORCID: https://orcid.org/0000-0003-3007-250X)
- Mehrdad Dadgostar (ORCID: https://orcid.org/0000-0002-3238-1261)
- Maria Angela Franceschini (ORCID: https://orcid.org/0000-0001-6758-423X)
- Mitchell B. Robinson (ORCID: https://orcid.org/0000-0001-5810-0459)
- Kuan-Cheng Wu (ORCID: https://orcid.org/0000-0002-7794-8022)
- Jason Zhensheng Qu (ORCID: https://orcid.org/0000-0001-7586-8312)
- John Sunwoo (ORCID: https://orcid.org/0000-0003-4314-7232)
- Zahra Einalou
- Marco Renna (ORCID: https://orcid.org/0000-0002-6642-7360)
Institutions
- Harvard University (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-09-19
- DOI
- https://doi.org/10.1117/1.jbo.30.s2.s23913
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00