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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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Infertility in Females01:28

Infertility in Females

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Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of...
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Fertilization01:38

Fertilization

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During fertilization, an egg and sperm cell fuse to create a new diploid structure. In humans, the process occurs once the egg has been released from the ovary, and travels into the fallopian tubes. The process requires several key steps: 1) sperm present in the genital tract must locate the egg; 2) once there, sperm need to release enzymes to help them burrow through the protective zona pellucida of the egg; and 3) the membranes of a single sperm cell and egg must fuse, with the sperm...
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Meiosis II01:57

Meiosis II

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Meiosis II is the second and final stage of meiosis. It relies on the haploid cells produced during meiosis I, each of which contain only 23 chromosomes—one from each homologous initial pair. Importantly, each chromosome in these cells is composed of two joined copies, and when these cells enter meiosis II, the goal is to separate such sister chromatids using the same microtubule-based network employed in other division processes. The result of meiosis II is two haploid cells, each...
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Current progress and open challenges for applying artificial intelligence across the in vitro fertilization cycle.

Yuanxu Gao1,2, Yifeng Yuan3,4,5,6, Kai Wang1

  • 1Department of Big Data and Biomedical AI, College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing 100871, China.

Patterns (New York, N.Y.)
|December 2, 2025
PubMed
Summary

Artificial intelligence (AI) can enhance decision-making in in vitro fertilization (IVF) treatments by analyzing diverse data. Challenges remain in AI generalizability, bias, and validation for equitable implementation.

Keywords:
artificial intelligenceclinical validationdeep learningembryo selectionin vitro fertilizationmachine learningmodel generalizationomics data analysis

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Biomedical Data Science

Background:

  • In vitro fertilization (IVF) success rates are limited by complexity and clinical experience.
  • Artificial intelligence (AI) presents opportunities to improve IVF decision-making.
  • A comprehensive review of AI applications in IVF is needed.

Purpose of the Study:

  • To review current AI applications in IVF, categorized by data modality.
  • To assess AI model performance and methodological advancements.
  • To explore emerging AI approaches and identify implementation challenges.

Main Methods:

  • Systematic review of studies applying AI in IVF.
  • Categorization of AI applications based on data types: structured health records, biomedical images, and omics data.
  • Analysis of AI model performance, task representation, and methodological progress.

Main Results:

  • AI applications are diverse across structured data, imaging, and omics in IVF.
  • Emerging AI methods like multi-modal learning and large language models show potential.
  • Key challenges include limited generalizability, data bias, and the need for clinical validation.

Conclusions:

  • AI integration in IVF holds significant promise for improving treatments.
  • Rigorous validation, ethical considerations, and transparency are crucial for successful AI implementation.
  • Interdisciplinary collaboration is essential for safe and equitable AI adoption in IVF.