2022
DOI: 10.1364/boe.452731
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Estimating intracranial pressure using pulsatile cerebral blood flow measured with diffuse correlation spectroscopy: erratum

Abstract: In our original published article, we labeled the x-axis in Fig. 1(b) incorrectly [Biomed. Opt. Express 11, 1462 (2020)10.1364/BOE.386612]. The sub-figure reports the importance of features extracted from the waveforms in training a machine learning algorithm to estimate intracranial pressure. This erratum corrects the labels in Fig. 1(b). The discussion and conclusions drawn from this article did not change.

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Cited by 5 publications
(7 citation statements)
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“…A source of uncertainty in this initial optical approach is the necessity for arterial blood pressure pulsatility measurements, which were estimated by systolic and diastolic cuff measurements. Application of advanced machine learning models may enable ICP prediction based on standalone DCS BFI or FD DOS measurements of trends in oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations [ 252 , 253 , 256 ]. These results highlight the feasibility and potential utility for a point-of-care noninvasive optical assessment of ICP in emergent settings and as a continuous bedside monitor to improve detection and management of intracranial hypertension.…”
Section: Advances In Neuromonitoringmentioning
confidence: 99%
“…A source of uncertainty in this initial optical approach is the necessity for arterial blood pressure pulsatility measurements, which were estimated by systolic and diastolic cuff measurements. Application of advanced machine learning models may enable ICP prediction based on standalone DCS BFI or FD DOS measurements of trends in oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations [ 252 , 253 , 256 ]. These results highlight the feasibility and potential utility for a point-of-care noninvasive optical assessment of ICP in emergent settings and as a continuous bedside monitor to improve detection and management of intracranial hypertension.…”
Section: Advances In Neuromonitoringmentioning
confidence: 99%
“…These statistics were calculated from pairs of observations (invasive and non-invasive ICP), treating each pair as an independent measurement. The latter is to ensure comparability of our results with other publications in the field, where AI models have been created from optical cerebral measurements to estimate ICP [ 30 , 39 , 46 ]. Nonetheless, the AI and statistical techniques applied have not been adjusted for the extensive number of repeated measurements per patient.…”
Section: Methodsmentioning
confidence: 83%
“…have been working with NIRS and diffuse correlation spectroscopy (DCS) technologies to estimate ICP non-invasively. The authors present a recompilation of studies where ICP changes were induced incrementally (from about 3–10 mmHg up to 40 mmHg in steps of 10 mmHg) through fluid infusion in non-human primates ( n = 5 to 8) [ 30 , 39 , 46 ]. These studies utilised the cardiac pulse acquired by DCS in a similar way to how this pilot trial used cerebral PPG signals.…”
Section: Discussionmentioning
confidence: 99%
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“…Diffuse optics methods (including NIRS and DCS) may provide a viable non-invasive method for ICP measurement. Early studies have examined their use in non-human primate (NHP) models 106 , 107 as well as in infants, 108 by estimating ICP from the derived cardiac waveform 106 and comparing this to the gold-standard invasive monitors. Importantly, alterations to relative [HbO] alone (which can be obtained with basic NIRS devices) appear to change with ICP and when used in combination with machine learning, can be used to derive ICP from waveform features in NHP with validation against invasive neuromonitors and CBF data from DCS 109 …”
Section: Challenges and Future Applicationsmentioning
confidence: 99%