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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...

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Combined Supine and Standing Imaging for Varicocele: An Improved Diagnostic Approach

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Published on: November 22, 2024

Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A

Jesse Ory^1,2, Michael B Tradewell¹, Udi Blankstein³

¹Department of Urology, University of Miami, Coral Gables, FL, USA.

The World Journal of Men'S Health

|January 12, 2022

Summary

This summary is machine-generated.

An artificial intelligence (AI) model accurately predicts which men with varicocele will benefit from surgical repair, improving fertility outcomes. This tool aids in personalized preoperative counseling for infertility treatment.

Keywords:

Artificial intelligence Fertility Patient-specific modeling Sperm count Varicocele

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Area of Science:

Urology
Reproductive Medicine
Artificial Intelligence

Background:

Varicocele repair is recommended for male infertility with abnormal semen parameters.
However, up to 50% of men do not experience significant benefits post-surgery.
Predicting treatment success is crucial for patient selection.

Purpose of the Study:

To develop an artificial intelligence (AI) model to predict which men with varicocele will benefit from surgical repair.
To identify factors influencing post-operative sperm parameter improvement.

Main Methods:

Retrospective analysis of 240 men with infertility and varicocele from two North American centers (2006-2020).
Collected pre- and post-operative clinical, hormonal, and semen analysis data.
Developed and validated a random forest AI model to predict sperm concentration upgrade.

Main Results:

45.6% of men showed improved sperm concentration post-surgery.
The AI model accurately predicted upgrade in 87% of men likely to improve (AUC 0.72).
Model performance was lower for equivocal (49%) and unlikely (36%) groups.

Conclusions:

A machine learning model effectively predicts post-varicocelectomy sperm parameter improvements using pre-operative data.
This is the first model utilizing hormonal data and machine learning for predicting clinically meaningful upgrading.
The model will be available as a clinical calculator for preoperative patient counseling.