ArticleAnatolian journal of cardiology2023
Application of Artificial Intelligence-Based Auxiliary Diagnosis in Congenital Heart Disease Screening.
Article in Anatolian journal of cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Rational and design of the digital diagnosis of cardiac sounds in paediatric patients (DI_SOUND) study.European heart journal. Digital health · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?Journal of clinical medicine · 2024Review
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- A systematic review on deep learning in ventricular septal defect screening, diagnosis, and management.Annals of pediatric cardiologyReview
- Accuracy of deep learning-based heart sound analysis as screening tool for congenital heart disease: A diagnostic meta-analysis.Annals of pediatric cardiologyArticle
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Authors and funding
5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease.
methodsFrom May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared.
resultsFemale sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032).
conclusionArtificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.
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