Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

Jintai Chen, Shuai Huang, Ying Zhang, Qing Chang, Yixiao Zhang, Dantong Li, Jia Qiu, Lianting Hu, Xiaoting Peng, Yunmei Du, Yunfei Gao, Danny Z. Chen, Abdelouahab Bellou (), Jian Wu () and Huiying Liang ()
Additional contact information
Jintai Chen: Zhejiang University
Shuai Huang: Southern Medical University
Ying Zhang: Guangdong Academy of Medical Sciences
Qing Chang: Liaoning Engineering Research Center of Intelligent Diagnosis and Treatment Ecosystem
Yixiao Zhang: Liaoning Engineering Research Center of Intelligent Diagnosis and Treatment Ecosystem
Dantong Li: Southern Medical University
Jia Qiu: Guangdong Academy of Medical Sciences
Lianting Hu: Southern Medical University
Xiaoting Peng: Southern Medical University
Yunmei Du: Guangzhou College of Commerce
Yunfei Gao: Jinan University
Danny Z. Chen: University of Notre Dame
Abdelouahab Bellou: Guangdong Academy of Medical Sciences
Jian Wu: Zhejiang University
Huiying Liang: Southern Medical University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-44930-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44930-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-44930-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().