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

Cheredath, A and Uppangala, S and Asha, CS and Jijo, A and Vani Lakshmi, R and Kumar, P and Joseph, D and GA, NG and Kalthur, G and Adiga, SK (2022) Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction. In: Reproductive Sciences .

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Official URL: https://doi.org/10.1007/s43032-022-01071-1


This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100 accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.

Item Type: Journal Article
Publication: Reproductive Sciences
Publisher: Institute for Ionics
Additional Information: The copyright for this article belongs to the Author(s).
Keywords: ANN; Blastocyst; Machine learning; Metabolomics; NMR spectroscopy
Department/Centre: Division of Chemical Sciences > NMR Research Centre (Formerly Sophisticated Instruments Facility)
Date Deposited: 31 Oct 2022 09:28
Last Modified: 31 Oct 2022 09:28
URI: https://eprints.iisc.ac.in/id/eprint/77680

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