ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2025
Development and Validation of a Novel Deep Learning Model to Predict Pharmacologic Closure of Patent Ductus Arteriosus in Premature Infants.
Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.Circulation · 2026Article
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease.medRxiv : the preprint server for health sciences · 2026Article
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence for patent ductus arteriosus-a systematic review.Frontiers in pediatrics · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundPatent ductus arteriosus (PDA) is associated with significant morbidity and mortality in preterm infants. Although pharmacotherapy can be effective, it is difficult to predict whether a patient will respond, leading to delays in care. Machine learning has emerged as a powerful tool to interpret clinical data to predict clinical outcomes but has not yet been applied to this question. The aim of this study was to train and validate a novel deep learning model to predict the likelihood of PDA closure after an initial course of pharmacotherapy in preterm infants.
methodsA retrospective cohort of 174 preterm infants who received pharmacologic treatment for PDA was identified. After collecting relevant perinatal data and pretreatment echocardiograms, the subjects were randomized into training and validation sets in a 70:30 split. Two distinct convolutional neural networks (CNN) were trained, one based on echocardiograms alone and the other on both echocardiograms and perinatal data. The performance of the CNNs was compared against controls of random forest and logistic regression models trained on perinatal data alone.
resultsThe rate of PDA closure after an initial course of pharmacotherapy was 60% in this cohort. The 174 echocardiograms collected for all subjects included 1,926 clips. A total of 121 infants (1,387 clips) were successfully randomized into the training set and 53 (539 clips) into the validation set. The multimodal CNN had an area under the curve (AUC) of 0.82, outperforming the imaging-only model (AUC = 0.66). Additionally, the multimodal CNN outperformed logistic regression (AUC = 0.66) and random forest (AUC = 0.74) models.
conclusionsThis novel, multimodal CNN shows promise for clinicians, who do not currently have a reliable tool to predict the success of PDA closure after an initial course of pharmacotherapy. This investigation represents the first attempt to use deep learning methodology to predict this outcome.
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