Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study

BACKGROUND: Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propo...

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Detalles Bibliográficos
Autores principales: Miura, Kotaro, Yagi, Ryuichiro, Miyama, Hiroshi, Kimura, Mai, Kanazawa, Hideaki, Hashimoto, Masahiro, Kobayashi, Sayuki, Nakahara, Shiro, Ishikawa, Tetsuya, Taguchi, Isao, Sano, Motoaki, Sato, Kazuki, Fukuda, Keiichi, Deo, Rahul C., MacRae, Calum A., Itabashi, Yuji, Katsumata, Yoshinori, Goto, Shinichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518511/
https://www.ncbi.nlm.nih.gov/pubmed/37753448
http://dx.doi.org/10.1016/j.eclinm.2023.102141

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518511/
https://www.ncbi.nlm.nih.gov/pubmed/37753448
http://dx.doi.org/10.1016/j.eclinm.2023.102141

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