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Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction.

Aswathi Cheredath1, Shubhashree Uppangala2, Asha C S3

  • 1Division of Clinical Embryology, Department of Reproductive Science, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576 104, India.

Reproductive Sciences (Thousand Oaks, Calif.)
|September 13, 2022
PubMed
Summary

Combining metabolomic data and embryologic parameters with machine learning (ML) models significantly enhances the prediction of embryo implantation potential. A custom artificial neural network (ANN) model achieved 100% accuracy, offering clinical benefits.

Keywords:
ANNBlastocystMachine learningMetabolomicsNMR spectroscopy

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Area of Science:

  • Reproductive Medicine and Biology
  • Biochemistry and Metabolomics
  • Artificial Intelligence in Healthcare

Background:

  • Predicting embryo implantation potential is crucial for successful in vitro fertilization (IVF).
  • Current methods often lack sufficient accuracy, leading to suboptimal treatment outcomes.
  • Integrating multi-omics data with advanced computational models offers a promising avenue for improved prediction.

Purpose of the Study:

  • To evaluate if combining metabolomic profiles from spent culture medium (SCM) with embryologic data improves embryo implantation prediction.
  • To assess the efficacy of machine learning (ML) models, including artificial neural networks (ANN), in predicting implantation potential.
  • To explore the clinical utility of a novel predictive approach for infertile couples undergoing IVF.

Main Methods:

  • Prospective cohort study involving 56 infertile couples undergoing day-5 single blastocyst transfer.
  • Spent culture medium (SCM) analysis using nuclear magnetic resonance (NMR) spectroscopy to identify metabolite levels.
  • Integration of SCM metabolite data and embryologic parameters into various ML models, including a custom ANN.

Main Results:

  • Blastocysts leading to successful implantation showed significantly lower pyruvate and threonine levels in SCM compared to controls.
  • Combining metabolomic and embryologic data with ML algorithms substantially increased prediction accuracy.
  • A custom artificial neural network (ANN) model incorporating metabolomic data achieved 100% accuracy in predicting implantation potential.

Conclusions:

  • The integration of metabolomic data, embryologic parameters, and advanced ML models, particularly custom ANNs, significantly enhances the prediction of embryo implantation.
  • This data-driven approach demonstrates high efficiency and holds potential for real-time clinical application to benefit patients undergoing IVF.
  • The study highlights the power of multi-modal data analysis in improving reproductive outcomes.