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Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS

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Bibliographic Details
Main Authors: Ludmilla, Penarrubia, Aude, Verstraete, Maciej, Orkisz
Format: Book
Language:English
Published: Springer 2023
Subjects:
computed tomography
positive end-expiratory pressure
Online Access:https://link.springer.com/article/10.1186/s40635-023-00495-6
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7047
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spelling oai:localhost:PNK-70472023-03-22T03:03:27Z Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS Ludmilla, Penarrubia Aude, Verstraete Maciej, Orkisz computed tomography positive end-expiratory pressure CC BY Assessing measurement error in alveolar recruitment on computed tomography (CT) is of paramount importance to select a reliable threshold identifying patients with high potential for alveolar recruitment and to rationalize positive end-expiratory pressure (PEEP) setting in acute respiratory distress syndrome (ARDS). The aim of this study was to assess both intra- and inter-observer smallest real difference (SRD) exceeding measurement error of recruitment using both human and machine learning-made lung segmentation (i.e., delineation) on CT. This single-center observational study was performed on adult ARDS patients. CT were acquired at end-expiration and end-inspiration at the PEEP level selected by clinicians, and at end-expiration at PEEP 5 and 15 cmH2O. 2023-03-22T03:03:27Z 2023-03-22T03:03:27Z 2023 Book https://link.springer.com/article/10.1186/s40635-023-00495-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7047 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic computed tomography
positive end-expiratory pressure
spellingShingle computed tomography
positive end-expiratory pressure
Ludmilla, Penarrubia
Aude, Verstraete
Maciej, Orkisz
Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
description CC BY
format Book
author Ludmilla, Penarrubia
Aude, Verstraete
Maciej, Orkisz
author_facet Ludmilla, Penarrubia
Aude, Verstraete
Maciej, Orkisz
author_sort Ludmilla, Penarrubia
title Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
title_short Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
title_full Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
title_fullStr Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
title_full_unstemmed Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS
title_sort precision of ct-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ards
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1186/s40635-023-00495-6
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7047
_version_ 1761097140392689664
score 8.891145

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