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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...

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Blastocyst selection through an interpretable artificial intelligence method versus traditional morphology grading:

Shanshan Wang1, Lei Chen1, Guanqiao Shan2

  • 1Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.

BMJ Open
|July 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an interpretable artificial intelligence (AI) method for selecting the best embryo for in vitro fertilization (IVF). The goal is to improve IVF success rates by providing transparent AI-driven insights for embryologists.

Keywords:
Artificial IntelligenceEmbryologyReproductive medicine

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Embryology

Background:

  • Blastocyst quality is crucial for in vitro fertilization (IVF) success.
  • Current human assessment of blastocysts is subjective and inconsistent.
  • Existing AI methods for IVF lack interpretability, raising ethical concerns.

Purpose of the Study:

  • To develop and evaluate a novel, interpretable artificial intelligence (AI) method for blastocyst selection.
  • To enhance the transparency and clinical applicability of AI in IVF procedures.
  • To improve in vitro fertilization (IVF) outcomes through more reliable embryo selection.

Main Methods:

  • A single-centre, single-blind randomized controlled trial (RCT) involving 1100 women undergoing their first IVF cycle.
  • Participants aged 20-35 years with at least two usable blastocysts will be randomized.
  • Comparison between conventional morphology assessment and the novel interpretable AI group.

Main Results:

  • The primary outcome is ongoing pregnancy at 12 weeks gestation.
  • The study aims to determine if the interpretable AI method improves IVF success rates compared to conventional methods.
  • Data collection and analysis are ongoing.

Conclusions:

  • An interpretable AI method has been developed for blastocyst selection in IVF.
  • This novel approach aims to address the limitations of subjective human assessment and opaque AI algorithms.
  • The RCT will provide crucial data on the clinical effectiveness of this transparent AI tool in improving IVF outcomes.