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Application of machine learning in screening for congenital heart diseases using fetal echocardiography

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Abstract

There is a growing body of literature supporting the utilization of machine learning (ML) to improve diagnosis and prognosis tools of cardiovascular disease. The current study was to investigate the impact that the ML framework may have on the sensitivity of predicting the presence or absence of congenital heart disease (CHD) using fetal echocardiography. A comprehensive fetal echocardiogram including 2D cardiac chamber quantification, valvar assessments, assessment of great vessel morphology, and Doppler-derived blood flow interrogation was recorded. The postnatal echocardiogram was used to ascertain the diagnosis of CHD. A random forest (RF) algorithm with a nested tenfold cross-validation was used to train models for assessing the presence of CHD. The study population was derived from a database of 3910 singleton fetuses with maternal age of 28.8 ± 5.2 years and gestational age at the time of fetal echocardiography of 22.0 weeks (IQR 21–24). The proportion of CHD was 14.1% for the studied cohort confirmed by post-natal echocardiograms. Our proposed RF-based framework provided a sensitivity of 0.85, a specificity of 0.88, a positive predictive value of 0.55 and a negative predictive value of 0.97 to detect the CHD with the mean of mean ROC curves of 0.94 and the mean of mean PR curves of 0.84. Additionally, six first features, including cardiac axis, peak velocity of blood flow across the pulmonic valve, cardiothoracic ratio, pulmonary valvar annulus diameter, right ventricular end-diastolic diameter, and aortic valvar annulus diameter, are essential features that play crucial roles in adding more predictive values to the model in detecting patients with CHD. ML using RF can provide increased sensitivity in prenatal CHD screening with very good performance. The incorporation of ML algorithms into fetal echocardiography may further standardize the assessment for CHD.

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Data availability

Requests for data access can be sent to the corresponding author at lekimtuyen09@gmail.com or vien.truong@thechristhospital.com.

References

  1. Hoffman JI, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39(12):1890–1900

    Article  Google Scholar 

  2. Zhang YF, Zeng XL, Zhao EF, Lu HW (2015) Diagnostic value of fetal echocardiography for congenital heart disease: a systematic review and meta-analysis. Medicine (Baltimore) 94(42):e1759

    Article  Google Scholar 

  3. Ailes EC, Gilboa SM, Honein MA, Oster ME (2015) Estimated number of infants detected and missed by critical congenital heart defect screening. Pediatrics 135(6):1000–1008

    Article  Google Scholar 

  4. Donofrio MT, Moon-Grady AJ, Hornberger LK, Copel JA, Sklansky MS, Abuhamad A et al (2014) Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation 129(21):2183–2242

    Article  Google Scholar 

  5. Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH et al (2017) Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J 38(7):500–507

    PubMed  Google Scholar 

  6. Al’Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z et al (2020) Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J 41(3):359–367

    Article  Google Scholar 

  7. Ruijsink B, Puyol-Anton E, Oksuz I, Sinclair M, Bai W, Schnabel JA et al (2020) Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function. JACC Cardiovasc Imaging 13(3):684–695

    Article  Google Scholar 

  8. Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L et al (2018) Fully automated echocardiogram interpretation in clinical practice. Circulation 138(16):1623–1635

    Article  Google Scholar 

  9. Rychik J, Ayres N, Cuneo B, Gotteiner N, Hornberger L, Spevak PJ et al (2004) American Society of Echocardiography guidelines and standards for performance of the fetal echocardiogram. J Am Soc Echocardiogr 17(7):803–810

    Article  Google Scholar 

  10. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    Google Scholar 

  11. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  12. Oppo K, Leen E, Angerson WJ, Cooke TG, McArdle CS (1998) Doppler perfusion index: an interobserver and intraobserver reproducibility study. Radiology 208(2):453–457

    Article  CAS  Google Scholar 

  13. Chang RK, Gurvitz M, Rodriguez S (2008) Missed diagnosis of critical congenital heart disease. Arch Pediatr Adolesc Med 162(10):969–974

    Article  Google Scholar 

  14. Kuehl KS, Loffredo CA, Ferencz C (1999) Failure to diagnose congenital heart disease in infancy. Pediatrics 103(4 Pt 1):743–747

    Article  CAS  Google Scholar 

  15. Mahle WT, Clancy RR, McGaurn SP, Goin JE, Clark BJ (2001) Impact of prenatal diagnosis on survival and early neurologic morbidity in neonates with the hypoplastic left heart syndrome. Pediatrics 107(6):1277–1282

    Article  CAS  Google Scholar 

  16. Bartlett JM, Wypij D, Bellinger DC, Rappaport LA, Heffner LJ, Jonas RA et al (2004) Effect of prenatal diagnosis on outcomes in D-transposition of the great arteries. Pediatrics 113(4):e335–e340

    Article  Google Scholar 

  17. Brown KL, Ridout DA, Hoskote A, Verhulst L, Ricci M, Bull C (2006) Delayed diagnosis of congenital heart disease worsens preoperative condition and outcome of surgery in neonates. Heart (Br Card Soc) 92(9):1298–1302

    Article  CAS  Google Scholar 

  18. Liu H, Zhou J, Feng QL, Gu HT, Wan G, Zhang HM et al (2015) Fetal echocardiography for congenital heart disease diagnosis: a meta-analysis, power analysis and missing data analysis. Eur J Prev Cardiol 22(12):1531–1547

    Article  Google Scholar 

  19. van Nisselrooij AEL, Teunissen AKK, Clur SA, Rozendaal L, Pajkrt E, Linskens IH et al (2020) Why are congenital heart defects being missed? Ultrasound Obstet Gynecol 55(6):747–757

    Article  Google Scholar 

  20. Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K et al (2016) Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2016;9(6):e004330.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP (2016) Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 68(21):2287–2295

    Article  Google Scholar 

  22. Arnaout R, Curran L, Chinn E, Zhao Y, MooŽ Grady AJ (2018) Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions. ArXiv. 2018;abs/1809.06993.

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Funding

The authors received no financial support for this study.

Author information

Author notes

  1. Vien T. Truong and Binh P. Nguyen have contributed equally to this work.

Authors and Affiliations

  1. The Christ Hospital Health Network, Cincinnati, OH, USA

    Vien T. Truong, Wojciech Mazur, Eugene S. Chung & Cassady Palmer

  2. The Lindner Research Center, Cincinnati, OH, USA

    Vien T. Truong

  3. School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand

    Binh P. Nguyen & Thanh-Hoang Nguyen-Vo

  4. Cincinnati Children’s Hospital Medical Center, University of Cincinnati, College of Medicine, Cincinnati, OH, USA

    Justin T. Tretter & Tarek Alsaied

  5. Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Vy T. Pham

  6. Heart Institute of HCMC, Ho Chi Minh City, Vietnam

    Huan Q. Do, Phuong T. N. Do, Ban N. Ha & Tuyen K. Le

  7. Heart Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam

    Vinh N. Pham

  8. University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam

    Hoa N. Chau

Authors
  1. Vien T. Truong
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  2. Binh P. Nguyen
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  3. Thanh-Hoang Nguyen-Vo
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  4. Wojciech Mazur
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  15. Tuyen K. Le
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Corresponding author

Correspondence to Tuyen K. Le.

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The authors declared no potential conflicts of interest of this study.

Ethical approval

The study was approved by the hospital’s Institutional Review Board.

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Informed consent was obtained from their parents/guardians. Assent was also obtained from children ages 7 and older.

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Truong, V.T., Nguyen, B.P., Nguyen-Vo, TH. et al. Application of machine learning in screening for congenital heart diseases using fetal echocardiography. Int J Cardiovasc Imaging 38, 1007–1015 (2022). https://doi.org/10.1007/s10554-022-02566-3

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  • DOI: https://doi.org/10.1007/s10554-022-02566-3

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