File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

TitleUsing Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients
Authors
Keywordsartificial intelligence
recurrent reproductive failure
reproductive immunology
sparse coding
assisted reproductive technology
Issue Date2021
PublisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/immunology
Citation
Frontiers in Immunology, 2021, v. 12, p. article no. 642167 How to Cite?
AbstractRecurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.
Persistent Identifierhttp://hdl.handle.net/10722/299775
ISSN
2020 Impact Factor: 7.561
2020 SCImago Journal Rankings: 2.646
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorHUANG, C-
dc.contributor.authorXiang, Z-
dc.contributor.authorZhang, Y-
dc.contributor.authorTan, DS-
dc.contributor.authorYip, CK-
dc.contributor.authorLiu, Z-
dc.contributor.authorLi, Y-
dc.contributor.authorYu, S-
dc.contributor.authorDiao, L-
dc.contributor.authorWong, LY-
dc.contributor.authorLing, WL-
dc.contributor.authorZeng, Y-
dc.contributor.authorTu, W-
dc.date.accessioned2021-05-26T03:28:53Z-
dc.date.available2021-05-26T03:28:53Z-
dc.date.issued2021-
dc.identifier.citationFrontiers in Immunology, 2021, v. 12, p. article no. 642167-
dc.identifier.issn1664-3224-
dc.identifier.urihttp://hdl.handle.net/10722/299775-
dc.description.abstractRecurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/immunology-
dc.relation.ispartofFrontiers in Immunology-
dc.rightsThis Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectrecurrent reproductive failure-
dc.subjectreproductive immunology-
dc.subjectsparse coding-
dc.subjectassisted reproductive technology-
dc.titleUsing Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients-
dc.typeArticle-
dc.identifier.emailTu, W: wwtu@hku.hk-
dc.identifier.authorityTu, W=rp00416-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fimmu.2021.642167-
dc.identifier.pmid33868275-
dc.identifier.pmcidPMC8047052-
dc.identifier.scopuseid_2-s2.0-85104265894-
dc.identifier.hkuros322499-
dc.identifier.volume12-
dc.identifier.spagearticle no. 642167-
dc.identifier.epagearticle no. 642167-
dc.publisher.placeSwitzerland-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats