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Deep learning identifies cardiac coupling between mother and fetus during gestation
In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372367/ https://www.ncbi.nlm.nih.gov/pubmed/35966548 http://dx.doi.org/10.3389/fcvm.2022.926965 |
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author | Alkhodari, Mohanad Widatalla, Namareq Wahbah, Maisam Al Sakaji, Raghad Funamoto, Kiyoe Krishnan, Anita Kimura, Yoshitaka Khandoker, Ahsan H. |
author_facet | Alkhodari, Mohanad Widatalla, Namareq Wahbah, Maisam Al Sakaji, Raghad Funamoto, Kiyoe Krishnan, Anita Kimura, Yoshitaka Khandoker, Ahsan H. |
author_sort | Alkhodari, Mohanad |
collection | PubMed |
description | In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices. |
format | Online Article Text |
id | pubmed-9372367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93723672022-08-13 Deep learning identifies cardiac coupling between mother and fetus during gestation Alkhodari, Mohanad Widatalla, Namareq Wahbah, Maisam Al Sakaji, Raghad Funamoto, Kiyoe Krishnan, Anita Kimura, Yoshitaka Khandoker, Ahsan H. Front Cardiovasc Med Cardiovascular Medicine In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9372367/ /pubmed/35966548 http://dx.doi.org/10.3389/fcvm.2022.926965 Text en Copyright © 2022 Alkhodari, Widatalla, Wahbah, Al Sakaji, Funamoto, Krishnan, Kimura and Khandoker. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Alkhodari, Mohanad Widatalla, Namareq Wahbah, Maisam Al Sakaji, Raghad Funamoto, Kiyoe Krishnan, Anita Kimura, Yoshitaka Khandoker, Ahsan H. Deep learning identifies cardiac coupling between mother and fetus during gestation |
title | Deep learning identifies cardiac coupling between mother and fetus during gestation |
title_full | Deep learning identifies cardiac coupling between mother and fetus during gestation |
title_fullStr | Deep learning identifies cardiac coupling between mother and fetus during gestation |
title_full_unstemmed | Deep learning identifies cardiac coupling between mother and fetus during gestation |
title_short | Deep learning identifies cardiac coupling between mother and fetus during gestation |
title_sort | deep learning identifies cardiac coupling between mother and fetus during gestation |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372367/ https://www.ncbi.nlm.nih.gov/pubmed/35966548 http://dx.doi.org/10.3389/fcvm.2022.926965 |
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