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Embryo ranking agreement between embryologists and artificial intelligence algorithms.

Nikica Zaninovic1, Jose T Sierra2, Jonas E Malmsten1

  • 1Weill Cornell Medicine, Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, New York, New York.

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|October 11, 2023
PubMed
Summary
This summary is machine-generated.

Embryologists showed high agreement in ranking embryo quality, significantly higher than artificial intelligence (AI) algorithms. Some AI models performed poorly, with agreement levels comparable to random chance, highlighting the need for further AI development in reproductive medicine.

Keywords:
Blastocyst scoreembryo rankinginterconcordance /intraconcordanceranking concordance

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Area of Science:

  • Reproductive Medicine and Embryology
  • Artificial Intelligence in Healthcare
  • Bioinformatics and Computational Biology

Background:

  • Embryo ranking is crucial for successful in vitro fertilization (IVF) outcomes.
  • Artificial intelligence (AI) offers potential for objective embryo assessment.
  • Evaluating AI performance against human expertise is essential for clinical adoption.

Purpose of the Study:

  • To compare the agreement of embryo quality ranking between human embryologists and multiple AI algorithms.
  • To assess the reliability and consistency of AI-driven embryo selection methods.
  • To identify potential discrepancies in AI performance and their implications for IVF.

Main Methods:

  • Retrospective analysis of 100 IVF cycles with at least eight embryos.
  • Embryo ranking by five embryologists and eight AI algorithms using time-lapse videos and static images.
  • Kendall rank correlation coefficient (Kendall's τ) used to measure agreement.

Main Results:

  • High inter-embryologist agreement (average Kendall's τ = 0.70) and intra-embryologist agreement (average K-τ = 0.78).
  • Significantly lower agreement between embryologists and AI algorithms (average K-τ = 0.53) and among AI algorithms (average K-τ = 0.47).
  • Two AI algorithms demonstrated very low agreement (average K-τ = 0.05), similar to random chance.

Conclusions:

  • This study is the first to compare embryo ranking agreement between AI and embryologists.
  • Intra- and inter-embryologist agreement surpassed AI-embryologist and inter-AI algorithm agreement.
  • Certain AI models showed agreement levels comparable to random selection, indicating a need for improvement.