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Infertility in Males

Infertility in Males

Male infertility affects millions of couples worldwide, arising from various factors that impact different stages of the reproductive process. An endocrine imbalance resulting from conditions like hypogonadism, Klinefelter syndrome, or pituitary disorders can disrupt hormone levels and reduce sperm production. Testicular defects, such as tumors, cryptorchidism, atrophic testes, abnormal sperm morphology, and low sperm count or motility, may arise due to genetic factors, structural...

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.
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Related Experiment Video

Updated: Jul 18, 2025

Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

Published on: April 10, 2018

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.

Debasmita GhoshRoy^1,2, P A Alvi³, K C Santosh^4,5

¹School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India.

Journal of Medical Systems

|August 23, 2023

Summary

Artificial intelligence (AI) tools can aid in the early detection of infertility, improving emotional well-being and reproductive health outcomes. This study analyzes AI

Keywords:

AI tools Infertility Risk Factors and Machine Learning

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Last Updated: Jul 18, 2025

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Human Egg Maturity Assessment and Its Clinical Application

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Published on: August 19, 2019

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Area of Science:

Reproductive Medicine
Artificial Intelligence
Biostatistics

Background:

Infertility significantly impacts social, marital, and emotional well-being.
Early diagnosis is crucial for managing infertility and its consequences.
Artificial intelligence (AI) offers potential for improved fertility assessment.

Conclusions:

AI holds significant potential to enhance the understanding and management of infertility.
Further research and development in AI are needed to optimize its application in reproductive health.
AI-driven insights can contribute to better mitigation strategies for infertility and its risk factors.