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Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease

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 ob...

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Autores principales: de Vries, Ivar R., van Laar, Judith O. E. H., van der Hout‐van der Jagt, Marieke B., Clur, Sally‐Ann B., Vullings, Rik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577634/
https://www.ncbi.nlm.nih.gov/pubmed/37563851
http://dx.doi.org/10.1111/aogs.14623
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author de Vries, Ivar R.
van Laar, Judith O. E. H.
van der Hout‐van der Jagt, Marieke B.
Clur, Sally‐Ann B.
Vullings, Rik
author_facet de Vries, Ivar R.
van Laar, Judith O. E. H.
van der Hout‐van der Jagt, Marieke B.
Clur, Sally‐Ann B.
Vullings, Rik
author_sort de Vries, Ivar R.
collection PubMed
description 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.
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spelling pubmed-105776342023-10-17 Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease de Vries, Ivar R. van Laar, Judith O. E. H. van der Hout‐van der Jagt, Marieke B. Clur, Sally‐Ann B. Vullings, Rik Acta Obstet Gynecol Scand Prenatal Diagnosis 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. John Wiley and Sons Inc. 2023-08-10 /pmc/articles/PMC10577634/ /pubmed/37563851 http://dx.doi.org/10.1111/aogs.14623 Text en © 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). https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Prenatal Diagnosis
de Vries, Ivar R.
van Laar, Judith O. E. H.
van der Hout‐van der Jagt, Marieke B.
Clur, Sally‐Ann B.
Vullings, Rik
Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title_full Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title_fullStr Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title_full_unstemmed Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title_short Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
title_sort fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease
topic Prenatal Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577634/
https://www.ncbi.nlm.nih.gov/pubmed/37563851
http://dx.doi.org/10.1111/aogs.14623
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