ArticleFrontiers in cardiovascular medicine2022
Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- Heart Murmur Classification Using a Capsule Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- A systematic review on deep learning in ventricular septal defect screening, diagnosis, and management.Annals of pediatric cardiologyReview
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients with faint murmurs or obesity. We aimed to develop an intelligent diagnostic method of detecting heart murmurs in patients with ventricular septal defects (VSDs) and atrial septal defects (ASDs). Materials and methods: Digital recordings of heart sounds and phonocardiograms of 184 participants were obtained. All participants underwent echocardiography by pediatric cardiologists to determine the type of CHD. The phonocardiogram data were classified as normal, ASD, or VSD. Then, the phonocardiogram signal was used to extract features to construct diagnostic models for disease classification using an advanced optical coherence tomography network (AOCT-NET). Cardiologists were asked to distinguish normal heart sounds from ASD/VSD murmurs after listening to the electronic sound recordings. Comparisons of the cardiologists' assessment and AOCT-NET performance were performed. Results: Echocardiography results revealed 88 healthy participants, 50 with ASDs, and 46 with VSDs. The AOCT-NET had no advantage in detecting VSD compared with cardiologist assessment. However, AOCT-NET performance was better than that of cardiologists in detecting ASD (sensitivity, 76.4 vs. 27.8%, respectively; specificity, 90 vs. 98.5%, respectively). Conclusion: The proposed method has the potential to improve the ASD detection rate and could be an important screening tool for patients without symptoms.
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