1. Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.
- Author
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de Vries IR, van Laar JOEH, van der Hout-van der Jagt MB, Clur SB, and Vullings R
- Subjects
- Pregnancy, Female, Infant, Newborn, Humans, Bayes Theorem, Ultrasonography, Prenatal methods, Electrocardiography, Fetal Heart diagnostic imaging, Artificial Intelligence, Heart Defects, Congenital diagnostic imaging
- Abstract
Introduction: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence., Material and Methods: An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance., Results: Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found., Conclusions: The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice., (© 2023 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).)
- Published
- 2023
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