This paper is in the following e-collection/theme issue:

Artificial Intelligence (1781) Machine Learning (1770) Pediatrics (236) Clinical Information and Decision Making (1688)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47612, first published .
Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study

Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study

Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study

Authors of this article:

Woocheol Jang1 Author Orcid Image ;   Yong Sung Choi2 Author Orcid Image ;   Ji Yoo Kim2 Author Orcid Image ;   Dong Keon Yon2 Author Orcid Image ;   Young Joo Lee3 Author Orcid Image ;   Sung-Hoon Chung4 Author Orcid Image ;   Chae Young Kim4 Author Orcid Image ;   Seung Geun Yeo5 Author Orcid Image ;   Jinseok Lee1 Author Orcid Image
Article Authors Cited by (6) Tweetations (6) Metrics

Journals

  1. Besiri K, Begou O, Lallas K, Kontou A, Agakidou E, Deda O, Gika H, Verykouki E, Sarafidis K. Gastric Fluid Metabolomics Predicting the Need for Surfactant Replacement Therapy in Very Preterm Infants Results of a Case–Control Study. Metabolites 2024;14(4):196 View
  2. Boskabadi H, Ataee Nakhaei M, Maamouri G, Saghafi N. Implications of maternal vitamin D administration for the neonatal respiratory distress syndrome: A randomized clinical trial. Journal of Neonatal-Perinatal Medicine 2024;17(2):183 View
  3. Liu C, Chen Y, Pan M, Lu X, Xu J, Chen X. Association between body mass index at birth and neonatal health outcomes in preterm infants: A retrospective analysis. Pediatric Obesity 2025;20(3) View
  4. Tadepalli K, Das A, Meena T, Roy S. Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges. Computer Methods and Programs in Biomedicine 2025;263:108682 View
  5. Giaxi P, Vivilaki V, Sarella A, Harizopoulou V, Gourounti K. Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery. Cureus 2025 View
  6. Cheng X, Tang J, Ge M, He Y, Li X, Xu Y, Wei H, Zhu D, Wang P, Pan H. Global landscape of neonatal disorders attributed to environmental and maternal risks over three decades. Ecotoxicology and Environmental Safety 2025;299:118411 View