ArticleBMC pediatrics2022
Machine learning for prediction of bronchopulmonary dysplasia-free survival among very preterm infants.
Article in BMC pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Clinical and Imaging Data-based Machine Learning for Early Diagnosis of Bronchopulmonary Dysplasia: A Meta-analysis.Current medical imaging · 2025Pooled it
- Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning.Pediatric research · 2026Article
- Video-based artificial intelligence for automated neonatal respiratory monitoring.European journal of pediatrics · 2026Article
- Lung Function Trajectories After Preterm Birth: A Life-Course Approach to Age-Specific Monitoring.Children (Basel, Switzerland) · 2026Review
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Development and validation of machine learning models for predicting bronchopulmonary dysplasia risk in preterm neonates based on antenatal determinants: a retrospective cohort study.BMC pediatrics · 2025Article
- Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.Scientific reports · 2025Article
- Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants.Frontiers in pediatrics · 2025Review
- Disruption of the gut bile acid-microbiota axis precedes severe bronchopulmonary dysplasia in preterm infants.Frontiers in microbiology · 2025Article
- Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.Pediatric pulmonology · 2024Article
- Synergistic effects of achieving perinatal interventions on bronchopulmonary dysplasia in preterm infants.European journal of pediatrics · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Focused review on artificial intelligence for disease detection in infants.Frontiers in digital health · 2024Review
- Prevention of Inflammatory Disorders in the Preterm Neonate: An Update with a Special Focus on Bronchopulmonary Dysplasia.Neonatology · 2024Review
- A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities.Frontiers in pediatrics · 2024Review
- Predictive analytics in bronchopulmonary dysplasia: past, present, and future.Frontiers in pediatrics · 2024Review
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
- Clinical characteristics of bronchopulmonary dysplasia in very preterm infants.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBronchopulmonary dysplasia (BPD) is one of the most common and serious sequelae of prematurity. Prompt diagnosis using prediction tools is crucial for early intervention and prevention of further adverse effects. This study aims to develop a BPD-free survival prediction tool based on the concept of the developmental origin of BPD with machine learning.
methodsDatasets comprising perinatal factors and early postnatal respiratory support were used for initial model development, followed by combining the two models into a final ensemble model using logistic regression. Simulation of clinical scenarios was performed.
resultsData from 689 infants were included in the study. We randomly selected data from 80% of infants for model development and used the remaining 20% for validation. The performance of the final model was assessed by receiver operating characteristics which showed 0.921 (95% CI: 0.899-0.943) and 0.899 (95% CI: 0.848-0.949) for the training and the validation datasets, respectively. Simulation data suggests that extubating to CPAP is superior to NIPPV in BPD-free survival. Additionally, successful extubation may be defined as no reintubation for 9 days following initial extubation.
conclusionsMachine learning-based BPD prediction based on perinatal features and respiratory data may have clinical applicability to promote early targeted intervention in high-risk infants.
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