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Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
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An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse

Bo Huang1, Wei Tan1, Zhou Li2

  • 1Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, 430030, Wuhan, People's Republic of China.

Reproductive Biology and Endocrinology : RB&E
|December 14, 2021
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model named EPA can predict embryo ploidy status using time-lapse technology (TLT) data. This non-invasive method aids in selecting the best embryo for in vitro fertilization and embryo transfer (IVF-ET).

Keywords:
AIPGTPloidy status, time-lapsePrediction

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Area of Science:

  • Reproductive medicine
  • Embryology
  • Artificial intelligence in healthcare

Background:

  • The association between time-lapse technology (TLT) and embryo ploidy status requires further understanding.
  • TLT offers non-invasive embryo assessment with large data potential.
  • Current artificial intelligence (AI) applications for predicting embryo ploidy from TLT data need enhancement.

Purpose of the Study:

  • To develop and validate an AI model for predicting embryo ploidy status using TLT data.
  • To assess the accuracy of the developed AI model in a clinical setting.

Main Methods:

  • Utilized data from 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts (April 2018-November 2019).
  • Embryo images were captured using a time-lapse microscope system before biopsy.
  • A dataset was divided into training, validation, and testing sets, with external verification using 155 PGT cycles and 523 blastocysts (December 2019-December 2020).

Main Results:

  • The developed euploid prediction algorithm (EPA) achieved an area under the curve (AUC) of 0.80 in predicting euploidy on the testing dataset.

Conclusions:

  • The AI model, EPA, demonstrates effective prediction of embryo ploidy status from TLT data.
  • This AI-driven, non-invasive approach can assist embryologists in selecting optimal embryos for in vitro fertilization and embryo transfer (IVF-ET).
  • The EPA system has the potential to benefit patients undergoing IVF-ET procedures.