ArticleJournal of medical Internet research2023
Artificial Intelligence-Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 20 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
- Emerging Applications of Artificial Intelligence in Pediatric Care.Indian journal of pediatrics · 2026Review
- Association between base excess levels and clinical outcomes among very low birth weight infants: a multicenter birth cohort study in South Korea.Clinical and experimental pediatrics · 2026Article
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Predicting neonatal respiratory distress syndrome in late preterm infants.BMC pregnancy and childbirth · 2026Article
- Surfactant therapy in neonatal care: advances, challenges, and practical considerations.European journal of pediatrics · 2026Review
- The influence of family care guidance for premature infants based on a telemedicine model on their neurodevelopmental assessment indicators.Frontiers in pediatrics · 2026Article
- Association between placental histological chorioamnionitis and respiratory distress syndrome in preterm infants: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- The association between family history of hypertension and diabetic kidney disease in patients with diabetes: a cross-sectional study.Frontiers in endocrinology · 2026Article
- Automated hypoxia and apnea identification for neonates via enhanced respiratory signal modeling with deep learning.Scientific reports · 2025Article
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- NeoCLIP: a self-supervised foundation model for the interpretation of neonatal radiographs.NPJ digital medicine · 2025Article
- Associated factors and clinical outcomes of feeding intolerance in preterm extremely low birthweight infants.Scientific reports · 2025Article
- Family history of non-communicable diseases and the risk of cardiovascular-kidney-metabolic syndrome.Scientific reports · 2025Article
- Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery.Cureus · 2025Review
- Gastric Fluid Metabolomics Predicting the Need for Surfactant Replacement Therapy in Very Preterm Infants Results of a Case-Control Study.Metabolites · 2024Article
- Artificial Intelligence-Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRespiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases.
objectiveWe aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment.
methodsIn this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed.
resultsOur proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed.
conclusionsOur artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant.
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