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Infertility in Males01:23

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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Machine Learning-Based Prediction of Sperm Retrieval Outcomes in Patients With Klinefelter Syndrome: A Multicenter

Murat Gül1,2, Ali Şahin1, Cevahir Özer3

  • 1Department of Urology, Selçuk University School of Medicine, Konya, Turkey.

Andrology
|July 1, 2026
PubMed
Summary

Machine learning accurately predicts sperm retrieval success in Klinefelter syndrome patients undergoing TESE. The Random Forest model, using hormone levels and testicular volume, offers robust prediction for male infertility management.

Keywords:
Klinefelter syndromeSHAPexternal validationmachine learningsperm retrievaltesticular sperm extraction

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Area of Science:

  • Reproductive Medicine
  • Genetics
  • Artificial Intelligence in Medicine

Background:

  • Klinefelter syndrome is a common genetic cause of male infertility.
  • Testicular sperm extraction (TESE) can retrieve sperm in some patients.
  • Predicting TESE success is difficult due to varied patient presentations.

Purpose of the Study:

  • Develop and validate machine learning (ML) models.
  • Predict sperm retrieval outcomes in Klinefelter syndrome patients.
  • Utilize routine clinical, hormonal, and testicular parameters.

Main Methods:

  • Multicenter retrospective study (470 patients).
  • Developed five ML algorithms (Random Forest, etc.).
  • Validated models using internal and external datasets, assessing performance metrics (AUROC, accuracy).

Main Results:

  • Ensemble models showed high internal performance (AUROC > 0.95).
  • Random Forest achieved best external validation (accuracy 0.83, AUROC 0.95).
  • Follicle-stimulating hormone, testosterone, LH, and testicular volume were key predictors.

Conclusions:

  • ML models accurately and interpretably predict TESE outcomes in Klinefelter syndrome.
  • Random Forest showed the most robust performance.
  • ML tools can aid in personalized infertility counseling and decision-making.