Artificial intelligence: how technology helps in the diagnosis of congenital heart disease
como a tecnologia auxilia no diagnóstico das cardiopatias congênitas
Keywords:
Heart Defects, Generative Artificial Intelligence, Prenatal Care.Abstract
Congenital heart disease (CHD) is the most common birth defect, which has a low rate of early detection. In such a scenario, AI shows promise, especially regarding the clinical-radiological impacts of fetal echocardiography. This systematic and qualitative literature review was prepared through bibliographic research in the Virtual Health Library (VHL) and PubMed databases. Although AI helps in the detection of CC and contributes to less experienced professionals, limitations are observed and its implementation requires clinical validation. The impact on reducing underdiagnosis and improving clinical outcomes is limited, depends on human assistance, and lacks studies to demonstrate the impact on fetal echocardiography.
References
Kim, Rina; Lee, Mi-Young; Lee, Yoo Jin; Won, Hye-Sung; Park, Jinki; Lee, Jihoon, et al. Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist. Sci Rep. 16 Abr 2025. Disponível em: https://pesquisa.bvsalud.org/portal/resource/pt/mdl-40240835?lang=en. Acesso: 20 Ago 2025.
Zhang, Junmin; Xiao, Sushan; Zhu, sim; Zhang, Zisang; Cao, Haiyan; Xie, Mingxing, et al. Advances in the Application of Artificial Intelligence in Fetal Echocardiography. Journal of the American Society of Echocardiography. 9 Jan 2024. Disponível em: https://pesquisa.bvsalud.org/portal/resource/pt/mdl-38199332?lang=en. Acesso: 20 Ago 2025.
Khadiza Tun Suha; Hugh Lubenow; Stefania Soria-Zurita; Marcus Haw; José Vettukattil; Jing Feng Jiang. The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review. Medicina. 21 Mar 2025 Mar 21. Disponível em: https://pubmed.ncbi.nlm.nih.gov/40282852/. Acesso: 20 Ago 2025.
Furong Li; Ping Li; Zhonghua Liu; Shunlan Liu; Pan Zeng; Canção de Haisheng, et al. Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images. BMC Pregnancy and Childbirth. 16 Nov 2024. Disponível em: https://pubmed.ncbi.nlm.nih.gov/39550543/. Acesso: 20 Ago 2025.
Day, T G; Matthew, J; Budd, S F; Venturini, L; Wright, R; Farruggia, A, et al. Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance? Ultrasound in Obstetrics and Gynecology. 3 Jun 2024. Disponível em: https://pubmed.ncbi.nlm.nih.gov/37776084/. Acesso: 20 Ago 2025.
Thomas G Day; Samuel Budd; Jeremy Tan; Jacqueline Matthew; Emily Skelton; Victoria Jowett, et al. Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme? Prenatal Diagnosis. 20 Sep 2023. Disponível em: https://pubmed.ncbi.nlm.nih.gov/40687738/. Acesso: 20 Ago 2025.
CA Taksøe-Vester; K Mikolaj; OBB Petersen; NG Vejlstrup; AN Christensen; Um Feragen, et al. Role of AI‐assisted automated cardiac biometrics in screening for fetal coarctation of aorta. Ultrasound in Obstetrics & Gynecology. 9 Fev 2024. Disponível em: https://pubmed.ncbi.nlm.nih.gov/38339776/. Acesso: 20 Ago 2025.
Drukker L. The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard‐to‐identify fetal cardiac anomalies? Ultrasound in Obstetrics & Gynecology. 5-9 Jun 2024. Disponível em: https://pesquisa.bvsalud.org/portal/resource/pt/mdl-38949769?lang=en. Acesso: 20 Ago 2025.
Zhang, Junmin; Xiao, Sushan; Zhu, Ye; Zhang, Zisang; Cao, Haiyan; Xie, Mingxing, et al. Advances in the Application of Artificial Intelligence in Fetal Echocardiography. Journal of the American Society of Echocardiography [Internet]. 9 Jan 2024. Disponível em: https://pesquisa.bvsalud.org/portal/resource/pt/mdl-38199332. Acesso: 20 Ago 2025.
Wiku Andonotopo; Muhammad Adrianes Bachnas; Muhammad Ilham Aldika Akbar; Muhammad Alamsyah Aziz; Julian Dewantiningrum; Mochammad Besari Adi Pramono, et al. Fetal origins of adult disease: transforming prenatal care by integrating Barker’s Hypothesis with AI-driven 4D ultrasound. Journal of Perinatal Medicine. 8 Abr 2025. Disponível em: https://pubmed.ncbi.nlm.nih.gov/40195943/. Acesso: 20 Ago 2025.
Ray Bahado-Singh; Nadia Ashrafi; Amin Ibrahim; Buket Aydas; Ali Yilmaz; Perry Friedman, et al. Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence. Scientific Reports. 15 Jan 2025. Disponível em: https://pubmed.ncbi.nlm.nih.gov/39814838/. Acesso: 20 Ago 2025.
Nathalie Jeanne Bravo-Valenzuela, Marcela Castro Giffoni, Caroline de Oliveira Nieblas, Heron Werner, Gabriele Tonni, Roberta Granese, et al. Three-Dimensional Ultrasound for Physical and Virtual Fetal Heart Models: Current Status and Future Perspectives. Journal of Clinical Medicine [Internet]. 13 Dez 2024. Disponível em: https://pubmed.ncbi.nlm.nih.gov/39768529/. Acesso: 20 Ago 2025.
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