Application research progress of artificial intelligence in echocardiography for congenital heart disease in children_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

MA Yujun , ZHAO Yudie ,  WU Kaihong

Department of Cardiothoracic Surgery, Children’s Hospital of Nanjing Medical University, Nanjing, 210000, P. R. China;

Corresponding?author：

WU Kaihong, Email: pumcwu@sina.com

Keywords：

Artificial intelligence technology; echocardiography; congenital heart disease; diagnosis; review

DOI：

10.7507/1007-4848.202508068

Video：

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[Abstract]Echocardiography is a vital diagnostic tool for congenital heart disease (CHD), but its interpretation relies heavily on manual labor, is time-consuming and labor-intensive, and suffers from diagnostic accuracy influenced by the skill level of the sonographer. Furthermore, pediatric hearts are small, have rapid heart rates, and exhibit poor patient compliance, further complicating the examination and creating a gap between clinical expectations and reality. In recent years, artificial intelligence (AI) technology, particularly machine learning, have emerged as powerful image analysis tools. They have demonstrated significant effectiveness in assisting echocardiographic detection of pediatric CHD, highlighting their clinical value. This article reviews the progress of AI applications in pediatric CHD echocardiography and identifies the challenges and issues encountered.

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2.	Zhang Y, Wang J, Zhao J, et al. Current status and challenges in prenatal and neonatal screening, diagnosis, and management of congenital heart disease in China. Lancet Child Adolesc Health, 2023, 7(7): 479-489.
3.	Nakayama M, Yagi R, Goto S. Deep learning applications in 12-lead electrocardiogram and echocardiogram. JMA J, 2025, 8(1): 102-112.
4.	Sun X, Yin Y, Yang Q, et al. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res, 2023, 28(1): 242.
5.	Zhang S, Kang C, Cui J, et al. Development of machine learning-based models to predict congenital heart disease: A matched case-control study. Int J Med Inform, 2025, 195: 105741.
6.	Zhang J, Xiao S, Zhu Y, et al. Advances in the application of artificial intelligence in fetal echocardiography. J Am Soc Echocardiogr, 2024, 37(5): 550-561.
7.	Barrios JP, Tison GH. Advancing cardiovascular medicine with machine learning: progress, potential, and perspective. Cell Rep Med, 2022, 3(12): 100869.
8.	Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography, 2023, 42(1): 2-9.
9.	Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol, 2023, 62(2): 185-194.
10.	He F, Wang Y, Xiu Y, et al. Artificial intelligence in prenatal ultrasound diagnosis. Front Med (Lausanne), 2021, 8: 729978.
11.	Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med, 2021, 27(5): 882-891.
12.	Edwards LA, Feng F, Iqbal M, et al. Machine learning for pediatric echocardiographic mitral regurgitation detection. J Am Soc Echocardiogr, 2023, 36(1): 96-104. e4.
13.	Baumgartner CF, Kamnitsas K, Matthew J, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging, 2017, 36(11): 2204-2215.
14.	Gong YX, Zhang YY. , Zhu HG, et al. Fetal congenital heart disease echocardiogram screening based on DGACNN: adversarial one-class classification combined with video transfer learning. IEEE Trans Med Imaging, 2020, 39(4): 1206-1222.
15.	Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med, 2018, 1: 6.
16.	Pu B, Lu Y, Chen J, et al. MobileUNet-FPN: a semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments. IEEE J Biomed Health Inform, 2022, 26(11): 5540-5550.
17.	Liang B, Peng F, Luo D, et al. Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2. BMC Med Inform Decis Mak, 2024, 24(1): 128.
18.	Lu Y, Li K, Pu B, et al. A YOLOX-based deep instance segmentation neural network for cardiac anatomical structures in fetal ultrasound images. IEEE/ACM Trans Comput Biol Bioinform, 2024, 21(4): 1007-1018.
19.	Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes. IEEE J Biomed Health Inform, 2020, 24(4): 931-942.
20.	Vandenhende S, Georgoulis S, Van Gansbeke W, et al. Multi-task learning for dense prediction tasks: a survey. IEEE Trans Pattern Anal Mach Intell, 2022, 44(7): 3614-3633.
21.	Zhang B, Liu H, Luo H, et al. Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning. Medicine (Baltimore), 2021, 100(4): e24427.
22.	Yu L, Guo Y, Wang Y, et al. Determination of fetal left ventricular volume based on two-dimensional echocardiography. J Healthc Eng, 2017, 2017: 4797315.
23.	Hamill N, Yeo L, Romero R, et al. Fetal cardiac ventricular volume, cardiac output, and ejection fraction determined with 4-dimensional ultrasound using spatiotemporal image correlation and virtual organ computer-aided analysis. Am J Obstet Gynecol, 2011, 205(1): 76.e1-10.
24.	Rizzo G, Capponi A, Pietrolucci ME, et al. Role of sonographic automatic volume calculation in measuring fetal cardiac ventricular volumes using 4-dimensional sonography: comparison with virtual organ computer-aided analysis. J Ultrasound Med, 2010, 29(2): 261-270.
25.	Adriaanse BM, Van Vugt JM, et al. Three- and four-dimensional ultrasound in fetal echocardiography: an up-to-date overview. J Perinatol, 2016, 36(9): 685-693.
26.	Reddy CD, Lopez L, Ouyang D, et al. Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients. J Am Soc Echocardiogr, 2023, 36(5): 482-489.
27.	Ufkes S, Zuercher M, Erdman L, et al. Automatic prediction of paediatric cardiac output from echocardiograms using deep learning model. CJC Pediatr Congenit Heart Dis, 2022, 2(1): 12-19.
28.	Zuercher M, Ufkes S, Erdman L, et al. Retraining an artificial intelligence algorithm to calculate left ventricular ejection fraction in pediatrics. J Cardiothorac Vasc Anesth, 2022, 36(9): 3610-3616.
29.	Hagai A, Anna E, Samantha T, et al. Comparing achievability and reproducibility of pulsed wave Doppler and tissue Doppler myocardial performance index and spatiotemporal image correlation annular plane systolic excursion in the cardiac function assessment of normal pregnancies. J Perinat Med, 2025, 53(2): 196-204.
30.	He BY, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
31.	Lin X, Yang F, Chen Y, et al. Echocardiography-based AI for detection and quantification of atrial septal defect. Front Cardiovasc Med, 2023, 10: 985657.
32.	Hong WJ, Sheng QY, Dong B, et al. Automatic detection of secundum atrial septal defect in children based on color doppler echocardiographic images using convolutional neural networks. Front Cardiovasc Med, 2022, 9: 834285.
33.	Meza JM, Slieker M, Blackstone EH, et al. A novel, data-driven conceptualization for critical left heart obstruction. Computer Methods Programs Biomed, 2018, 165: 107-116.
34.	Sun J, Feng TN, Wang B, et al. Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China. Lancet Digit Health, 2025, 7(1): e44-e53.
35.	Patey O, Hernandez-Cruz N, D'alberti E, et al. Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study. BMJ Open, 2025, 15(6): e101263.

1. Van Der Linde D, Konings EE, Slager MA, et, al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol, 2011, 58(21): 2241-2247.
2. Zhang Y, Wang J, Zhao J, et al. Current status and challenges in prenatal and neonatal screening, diagnosis, and management of congenital heart disease in China. Lancet Child Adolesc Health, 2023, 7(7): 479-489.
3. Nakayama M, Yagi R, Goto S. Deep learning applications in 12-lead electrocardiogram and echocardiogram. JMA J, 2025, 8(1): 102-112.
4. Sun X, Yin Y, Yang Q, et al. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res, 2023, 28(1): 242.
5. Zhang S, Kang C, Cui J, et al. Development of machine learning-based models to predict congenital heart disease: A matched case-control study. Int J Med Inform, 2025, 195: 105741.
6. Zhang J, Xiao S, Zhu Y, et al. Advances in the application of artificial intelligence in fetal echocardiography. J Am Soc Echocardiogr, 2024, 37(5): 550-561.
7. Barrios JP, Tison GH. Advancing cardiovascular medicine with machine learning: progress, potential, and perspective. Cell Rep Med, 2022, 3(12): 100869.
8. Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography, 2023, 42(1): 2-9.
9. Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol, 2023, 62(2): 185-194.
10. He F, Wang Y, Xiu Y, et al. Artificial intelligence in prenatal ultrasound diagnosis. Front Med (Lausanne), 2021, 8: 729978.
11. Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med, 2021, 27(5): 882-891.
12. Edwards LA, Feng F, Iqbal M, et al. Machine learning for pediatric echocardiographic mitral regurgitation detection. J Am Soc Echocardiogr, 2023, 36(1): 96-104. e4.
13. Baumgartner CF, Kamnitsas K, Matthew J, et al. SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging, 2017, 36(11): 2204-2215.
14. Gong YX, Zhang YY. , Zhu HG, et al. Fetal congenital heart disease echocardiogram screening based on DGACNN: adversarial one-class classification combined with video transfer learning. IEEE Trans Med Imaging, 2020, 39(4): 1206-1222.
15. Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med, 2018, 1: 6.
16. Pu B, Lu Y, Chen J, et al. MobileUNet-FPN: a semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments. IEEE J Biomed Health Inform, 2022, 26(11): 5540-5550.
17. Liang B, Peng F, Luo D, et al. Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2. BMC Med Inform Decis Mak, 2024, 24(1): 128.
18. Lu Y, Li K, Pu B, et al. A YOLOX-based deep instance segmentation neural network for cardiac anatomical structures in fetal ultrasound images. IEEE/ACM Trans Comput Biol Bioinform, 2024, 21(4): 1007-1018.
19. Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes. IEEE J Biomed Health Inform, 2020, 24(4): 931-942.
20. Vandenhende S, Georgoulis S, Van Gansbeke W, et al. Multi-task learning for dense prediction tasks: a survey. IEEE Trans Pattern Anal Mach Intell, 2022, 44(7): 3614-3633.
21. Zhang B, Liu H, Luo H, et al. Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning. Medicine (Baltimore), 2021, 100(4): e24427.
22. Yu L, Guo Y, Wang Y, et al. Determination of fetal left ventricular volume based on two-dimensional echocardiography. J Healthc Eng, 2017, 2017: 4797315.
23. Hamill N, Yeo L, Romero R, et al. Fetal cardiac ventricular volume, cardiac output, and ejection fraction determined with 4-dimensional ultrasound using spatiotemporal image correlation and virtual organ computer-aided analysis. Am J Obstet Gynecol, 2011, 205(1): 76.e1-10.
24. Rizzo G, Capponi A, Pietrolucci ME, et al. Role of sonographic automatic volume calculation in measuring fetal cardiac ventricular volumes using 4-dimensional sonography: comparison with virtual organ computer-aided analysis. J Ultrasound Med, 2010, 29(2): 261-270.
25. Adriaanse BM, Van Vugt JM, et al. Three- and four-dimensional ultrasound in fetal echocardiography: an up-to-date overview. J Perinatol, 2016, 36(9): 685-693.
26. Reddy CD, Lopez L, Ouyang D, et al. Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients. J Am Soc Echocardiogr, 2023, 36(5): 482-489.
27. Ufkes S, Zuercher M, Erdman L, et al. Automatic prediction of paediatric cardiac output from echocardiograms using deep learning model. CJC Pediatr Congenit Heart Dis, 2022, 2(1): 12-19.
28. Zuercher M, Ufkes S, Erdman L, et al. Retraining an artificial intelligence algorithm to calculate left ventricular ejection fraction in pediatrics. J Cardiothorac Vasc Anesth, 2022, 36(9): 3610-3616.
29. Hagai A, Anna E, Samantha T, et al. Comparing achievability and reproducibility of pulsed wave Doppler and tissue Doppler myocardial performance index and spatiotemporal image correlation annular plane systolic excursion in the cardiac function assessment of normal pregnancies. J Perinat Med, 2025, 53(2): 196-204.
30. He BY, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature, 2023, 616(7957): 520-524.
31. Lin X, Yang F, Chen Y, et al. Echocardiography-based AI for detection and quantification of atrial septal defect. Front Cardiovasc Med, 2023, 10: 985657.
32. Hong WJ, Sheng QY, Dong B, et al. Automatic detection of secundum atrial septal defect in children based on color doppler echocardiographic images using convolutional neural networks. Front Cardiovasc Med, 2022, 9: 834285.
33. Meza JM, Slieker M, Blackstone EH, et al. A novel, data-driven conceptualization for critical left heart obstruction. Computer Methods Programs Biomed, 2018, 165: 107-116.
34. Sun J, Feng TN, Wang B, et al. Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China. Lancet Digit Health, 2025, 7(1): e44-e53.
35. Patey O, Hernandez-Cruz N, D'alberti E, et al. Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study. BMJ Open, 2025, 15(6): e101263.

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