Observational studyCritical care (London, England)2024
Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.
Observational study in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.Critical care (London, England) · 2025Pooled it
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- PVADet: fast patient-ventilator asynchrony detection on waveforms.Journal of clinical monitoring and computing · 2026Article
- Integration, challenges, and future of artificial intelligence in critical care medicine: comprehensive applications from predictive models to clinical integration.Frontiers in medicine · 2026Review
- Assessing inspiratory drive and effort in critically ill patients at the bedside.Critical care (London, England) · 2025Review
- Leveraging large language models for patient-ventilator asynchrony detection.BMJ health & care informatics · 2025Article
- Let's get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony.Intensive care medicine experimental · 2025Review
- Setting the ventilator with AI support: challenges and perspectives.Intensive care medicine · 2025Article
- Bridging the Gap Between Detection, Understanding, and Future Innovation in Patient-Ventilator Asynchronies.Respiratory care · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 9 institutions in 4 countries.
Funding
Abstract
backgroundFlow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths.
methodsMulticenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔP
results6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔP
conclusionsRecurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.
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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.