ArticleAnnals of intensive care2023
Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan.
Article in Annals of intensive care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- PEEP and alveolar recruitment after 60 years of acute respiratory distress syndrome.Intensive care medicine · 2026Review
- Lung Imaging in Acute Hypoxemic Respiratory Failure: From Physics to Bedside Applications.Journal of clinical medicine · 2026Review
- Characterizing heterogeneity and subphenotyping acute respiratory distress syndrome with computed tomography.Intensive care medicine experimental · 2026Review
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Advances in Imaging and Physiology-Guided Personalized Care in Acute Respiratory Distress Syndrome.Medicina (Kaunas, Lithuania) · 2026Review
- Bedside ventilatory settings guided by respiratory mechanics in acute respiratory distress syndrome.Annals of intensive care · 2025Review
- Advances in noninvasive imaging for detecting radiation-induced lung injury (RILI).International journal of radiation biology · 2025Review
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Quantitative Computed Tomography and Response to Pronation in COVID-19 ARDS.Respiratory care · 2024Observational
- Lung Imaging and Artificial Intelligence in ARDS.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo develop and validate classifier models that could be used to identify patients with a high percentage of potentially recruitable lung from readily available clinical data and from single CT scan quantitative analysis at intensive care unit admission. 221 retrospectively enrolled mechanically ventilated, sedated and paralyzed patients with acute respiratory distress syndrome (ARDS) underwent a PEEP trial at 5 and 15 cmH
resultsML algorithms based on CT scan data at 5 cmH
conclusionsML based on a single CT data at 5 cmH
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.