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

Jintai ChenState Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, 310009, Hangzhou, ChinaShuai HuangMedical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, ChinaYing ZhangLiaoning Engineering Research Center of Intelligent Diagnosis and Treatment Ecosystem, 110004, Shenyang, Liaoning Province, ChinaQing ChangClinical Research Center of Shengjing Hospital of China Medical University, 110004, Shenyang, Liaoning Province, ChinaYixiao ZhangDepartment of Urology Surgery, Shengjing Hospital of China Medical University, 110004, Shenyang, Liaoning Province, ChinaDantong LiGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, ChinaJia QiuGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, ChinaLianting HuGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, ChinaXiaoting PengMedical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, ChinaYunmei DuGuangzhou College of CommerceYunfei GaoZhuhai Precision Medical Center, Zhuhai People's Hospital/ Zhuhai Hospital Affiliated with Jinan University, Jinan University, 519000, Zhuhai, Guangdong Province, ChinaDanny Z. ChenDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USAAbdelouahab BellouDepartment of Emergency Medicine, Wayne State University School of Medicine, Detroit, MI, 48201, USA. [email protected]Jian WuState Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, 310009, Hangzhou, China. [email protected]Huiying LiangMedical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China. [email protected]

2024en

ABI

Annotatsiya

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.

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DOI: 10.1038/s41467-024-44930-y

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