ArticleIntensive care medicine experimental2023
Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS.
Article in Intensive care medicine experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Diagnostic performance of the recruitment-to-inflation ratio to assess lung recruitability by PEEP in ARDS. a computed tomography study.Critical care (London, England) · 2025Observational
- Prognostic value of functional CT imaging in COVID-ARDS: a two-centre prospective observational study.Respiratory research · 2025Observational
- Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.Journal of imaging informatics in medicine · 2024Article
- Lung Imaging and Artificial Intelligence in ARDS.Journal of clinical medicine · 2024Review
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Authors and funding
17 authors.
Funding
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Abstract
backgroundAssessing 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 cmH
resultsThirteen patients were included, of whom 11 (85%) presented a severe ARDS. Intra- and inter-observer measurements of recruitment were virtually unbiased, with 95% confidence intervals (CI
conclusionsThe SRD exceeding intra-observer experimental error in the measurement of alveolar recruitment may be conservatively set to 5% (i.e., the upper value of the CI
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