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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ludmilla, Penarrubia | - |
dc.contributor.author | Aude, Verstraete | - |
dc.contributor.author | Maciej, Orkisz | - |
dc.date.accessioned | 2023-03-22T03:03:27Z | - |
dc.date.available | 2023-03-22T03:03:27Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://link.springer.com/article/10.1186/s40635-023-00495-6 | - |
dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7047 | - |
dc.description | CC BY | vi |
dc.description.abstract | 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. | vi |
dc.language.iso | en | vi |
dc.publisher | Springer | vi |
dc.subject | computed tomography | vi |
dc.subject | positive end-expiratory pressure | vi |
dc.title | Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS | vi |
dc.type | Book | vi |
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