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An artificial neural network for the prediction of assisted reproduction outcome

  • Assisted Reproduction Technologies
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Purpose

To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.

Methods

Data from 257 infertile couples that underwent a total of 426 IVF/ICSI cycles from 2010 to 2017 was collected on an ensemble of 118 parameters for each cycle. Statistical correlation of the parameters with the outcome of live birth was performed, using either t test or χ2 test, and the parameters that demonstrated statistical significance were used to construct the ANN. Cross-validation was performed by random separation of data and repeating the training-testing procedure by 10 times.

Results

12 statistically significant parameters out of the initial ensemble were used for the ANN construction, which exhibited a cumulative sensitivity and specificity of 76.7% and 73.4%, respectively. During cross-validation, the system exhibited the following: sensitivity 69.2% ± 2.36%, specificity 69.19% ± 2.8% (OR 5.21 ± 1.27), PPV 36.96 ± 3.44, NPV 89.61 ± 1.09, and OA 69.19% ± 2.69%. A rather small standard deviation in the performance indices between the training and test sets throughout the validation process indicated a stable performance of the constructed ANN.

Conclusions

The constructed ANN is based on statistically significant variables with the outcome of live birth and represents a stable and efficient system with increased performance indices. Validation of the system allowed an insight of its clinical value as a supportive tool in medical decisions, and overall provides a reliable approach in the routine practice of IVF units in a user-friendly environment.

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Acknowledgments

The authors wish to thank the Medical, Paramedical and Laboratory Team of the Assisted Reproduction Unit of “Attikon” University Hospital, Greece.

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Authors and Affiliations

  1. Assisted Reproduction Unit, Third Department of Obstetrics and Gynecology, Medical School, “Attikon” University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece

    Paraskevi Vogiatzi & Charalampos Siristatidis

  2. Second Department of Pathology, Medical School, “Attikon” University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece

    Abraham Pouliakis

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  1. Paraskevi Vogiatzi
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Correspondence to Charalampos Siristatidis.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Scientific Council and the Bioethics Committee of “Attikon” University Hospital (EVD 1172/26-11-15) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Capsule Summary: An Artificial Neural Network was constructed and cross-validated to predict live birth for patients undergoing assisted reproduction technologies, with demonstrated increased efficiency and stability.

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Vogiatzi, P., Pouliakis, A. & Siristatidis, C. An artificial neural network for the prediction of assisted reproduction outcome. J Assist Reprod Genet 36, 1441–1448 (2019). https://doi.org/10.1007/s10815-019-01498-7

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  • DOI: https://doi.org/10.1007/s10815-019-01498-7

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