Patent ductus arteriosus (PDA) detection in echocardiograms using deep learning

Background Patent ductus arteriosus (PDA) is a common form of congenital heart disease, especially in preterm infants. A PDA can be associated with prolonged ventilator dependence and increased risk of severe lung disease, necrotizing enterocolitis, impaired renal function, intraventricular hemorrhage, and death. The problem of caring for neonates with a PDA is difficult, and the use of artificial intelligence (AI) to aid in PDA detection can assist in its management. Methods A clinical database was searched for echocardiograms performed in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Orange County (CHOC) from 2017 to 21. A total of 461 de-identified echocardiogram video clips across 300 patients from CHOC were analyzed. Our goal was to explore the efficacy of the Convolutional Neural Network (CNN) for PDA detection in echocardiogram video clips for eventual clinical deployment on an edge-based device. To this end, we used the light-weight MobileNet-V2 CNN architecture for training and testing. Of the 461 echocardiogram video clips, 316 were used for training, 74 for validation, and 72 for testing. Video frames were extracted from each clip and processed by the CNN. The CNN treated the frames as independent images and performed binary (Normal vs PDA) classification on each video clip. Results and conclusion Of the 461 echocardiogram video clips analyzed, 272 contained an identifiable PDA and 190 were considered normal. Our CNN algorithm achieved notable results for identifying the presence of a PDA, with 0.88 Area Under Curve (AUC), 0.84 Positive Predictive Value (PPV), 0.80 Negative Predictive Value (NPV), 0.76 Sensitivity, and 0.87 Specificity on the test data. Results indicate that diagnosis of PDA within an edge-based AI framework is feasible. Future work will involve augmenting the echocardiogram dataset, expanding the analysis to include PDA classification based on size and hemodynamic significance, and exploring additional algorithmic approaches.