ArticleScientific reports2023
Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Video-based artificial intelligence for automated neonatal respiratory monitoring.European journal of pediatrics · 2026Article
- Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence algorithm to predict the requirement of neonatal endotracheal intubation within 3 h: application for clinical practice.Frontiers in medicine · 2026Article
- A three-factor nomogram predicts the use of invasive mechanical ventilation within 72 h in preterm infants.Frontiers in medicine · 2026Article
- Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.
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