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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

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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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Embryonic stem (ES) cells are undifferentiated pluripotent cells, meaning they can produce any cell type in the body. This gives them tremendous potential in science and medicine since they can generate specific cell types for use in research or to replace body cells lost due to damage or disease.
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Related Experiment Video

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Human Blastocyst Biopsy and Vitrification
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Federated task-adaptive learning for personalized selection of human IVF-derived embryos.

Tianrun Gao1, Yuning Yang1, Kai Wang2

  • 1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.

Communications Medicine
|November 18, 2025
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Summary

FedEmbryo, a novel AI system, enhances in-vitro fertilization (IVF) success by enabling privacy-preserving, decentralized embryo selection. It outperforms existing methods in morphological assessment and live-birth prediction.

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Data Privacy

Background:

  • In-vitro fertilization (IVF) success relies on manual embryo assessment, which is time-consuming and labor-intensive.
  • Artificial intelligence (AI) offers automated assessment but faces privacy challenges due to centralized data training.
  • Developing privacy-preserving AI for IVF is crucial for improving treatment efficacy and patient confidentiality.

Purpose of the Study:

  • To develop a distributed AI system, FedEmbryo, for personalized embryo selection in IVF.
  • To address data privacy concerns associated with centralized AI training in fertility treatments.
  • To enhance the accuracy and efficiency of embryo assessment for improved IVF outcomes.

Main Methods:

  • Developed FedEmbryo, a distributed AI system utilizing federated learning (FL) for privacy-preserving training.
  • Introduced a federated task-adaptive learning (FTAL) approach with hierarchical dynamic weighting adaptation (HDWA).
  • Integrated multitask learning (MTL) within FL to accommodate diverse learning tasks across clinical sites.

Main Results:

  • FedEmbryo demonstrated superior performance in morphological valuation of embryos compared to local models.
  • The system achieved higher accuracy in predicting live-birth outcomes in various IVF scenarios.
  • FedEmbryo outperformed state-of-the-art federated learning methods in experimental evaluations.

Conclusions:

  • FedEmbryo offers a privacy-preserving, decentralized AI solution to enhance IVF clinical decision-making.
  • The system excels at capturing stage-specific embryonic features and predicting key IVF outcomes.
  • FedEmbryo represents a practical advancement for improving IVF success rates through AI.