Trial reportScientific reports2021
Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.
Trial report in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 30 citations in OpenAlex.
- Novel Computational Model for Recapitulating Patent Ductus Arteriosus Stenting Interventions.Pediatric cardiology · 2026Article
- Artificial intelligence for patent ductus arteriosus-a systematic review.Frontiers in pediatrics · 2026Review
- Factors associated with allergic diseases in Chinese children aged 6-14 years.BMC public health · 2025Article
- Development and Validation of a Novel Deep Learning Model to Predict Pharmacologic Closure of Patent Ductus Arteriosus in Premature Infants.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2025Article
- Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.Medicina (Kaunas, Lithuania) · 2025Article
- Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.Scientific reports · 2025Article
- Machine learning-based prediction of hemodynamically significant patent ductus arteriosus in preterm neonates: a pioneering insight.Frontiers in pediatrics · 2025Article
- Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning.Scientific reports · 2024Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Risk stratification of hemodynamically significant patent ductus arteriosus by clinical and genetic factors.World journal of pediatrics : WJP · 2023Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- PD(AI): the role of artificial intelligence in the management of patent ductus arteriosus.Journal of perinatology : official journal of the California Perinatal Association · 2023Article
- Improving child health through Big Data and data science.Pediatric research · 2023Review
- Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study.Frontiers in pediatrics · 2023Article
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances.
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Registered trials
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.