Volume 9 Issue 6
Dec.  2023
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Jianhong Zhan, Chuangqi Chen, Na Zhang, Shuhuai Zhong, Jiaming Wang, Jinzhou Hu, Jiang Liu. An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology. Biophysics Reports, 2023, 9(6): 352-361. doi: 10.52601/bpr.2023.230035
Citation: Jianhong Zhan, Chuangqi Chen, Na Zhang, Shuhuai Zhong, Jiaming Wang, Jinzhou Hu, Jiang Liu. An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology. Biophysics Reports, 2023, 9(6): 352-361. doi: 10.52601/bpr.2023.230035

An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology

doi: 10.52601/bpr.2023.230035
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  • Corresponding author: zhanjianhong@ibp.ac.cn (J. Zhan); liujiang@ibp.ac.cn (J. Liu)
  • Received Date: 11 November 2023
  • Accepted Date: 28 November 2023
  • Available Online: 26 February 2024
  • Publish Date: 01 December 2023
  • Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimplantation DNA methylation screening (PIMS) revealed that whole genome DNA methylation level is a novel biomarker for assessing ART embryo quality. Here, we reinforced and estimated the clinical efficacy of PIMS. We introduce PIMS-AI, an innovative artificial intelligence (AI) based model, to predict the probability of an embryo producing live birth and subsequently assist ART embryo selection. Our model demonstrated robust performance, achieving an area under the curve (AUC) of 0.90 in cross-validation and 0.80 in independent testing. In simulated embryo selection, PIMS-AI attained an accuracy of 81% in identifying viable embryos for patients. Notably, PIMS-AI offers significant advantages over conventional preimplantation genetic testing for aneuploidy (PGT-A), including enhanced embryo discriminability and the potential to benefit a broader patient population. In conclusion, our approach holds substantial promise for clinical application and has the potential to significantly improve the ART success rate.

  • Jianhong Zhan, Chuangqi Chen, Na Zhang, Shuhuai Zhong, Jiaming Wang, Jinzhou Hu and Jiang Liu declare that they have no conflict of interest.
    This article does not contain any studies with human or animal subjects performed by any of the authors.
    Jianhong Zhan, Chuangqi Chen and Na Zhang contributed equally to this work.

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