ArticleEuropean journal of pediatrics2024
Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.
Article in European journal of pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 13 citations in OpenAlex.
- Beyond early diagnosis: toward AI-enabled multi-omics prediction of necrotizing enterocolitis.World journal of pediatrics : WJP · 2026Article
- Bifidobacterium modulates gut microbiota and aryl hydrocarbon receptor signaling in necrotizing enterocolitis rat model.Pediatric research · 2026Article
- Dual swin transformer for assisting in the diagnosis and surgical prediction of necrotizing enterocolitis.Pediatric research · 2026Article
- Deep learning feature-based model on abdominal radiography outperforms experts for early necrotizing enterocolitis diagnosis in neonates.European radiology · 2026Article
- Evaluation of a Clinical Risk Score for Preterm Necrotising Enterocolitis: The 'Check-NEC Score'.Journal of paediatrics and child health · 2026Article
- Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.Pediatric research · 2026Article
- Machine learning-based prognostic modeling of early clinical outcomes in very low birth weight infants: insights from a nationwide cohort on delivery room resuscitation, prematurity-related complications, and mortality.BMC medical informatics and decision making · 2026Article
- Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- Transfer learning prediction of surgical necrotizing enterocolitis in preterm infants without pneumoperitoneum on abdominal X-ray.Translational pediatrics · 2026Article
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- [Recent advances in predicting the surgical timing for neonatal necrotizing enterocolitis].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Surgical Necrotising Enterocolitis (S-NEC): Where We Stand Today: A Narrative Review.Journal of clinical medicine · 2026Review
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- Serum LRG1 as a diagnostic marker of necrotizing enterocolitis in preterm infants.Frontiers in pediatrics · 2026Article
- Validation of a novel Bayesian predictive algorithm for detection of carbon dioxide retention using retrospective neonatal ICU data.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Comparison of bedside abdominal ultrasonography and abdominal radiography in predicting surgical intervention in neonatal necrotising enterocolitis.Frontiers in pediatrics · 2026Article
- Machine Learning-Based Prediction of Surgical Intervention in Preterm Infants with Necrotizing Enterocolitis: A Retrospective Cohort Study.Children (Basel, Switzerland) · 2025Article
- [Recent advances in artificial intelligence for auxiliary diagnosis and management of neonatal necrotizing enterocolitis].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Surgical necrotizing enterocolitis risk factors in extremely preterm infants: a Korean nationwide cohort study.Pediatric research · 2025Article
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1 week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction. Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.
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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.