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Completed

NCT Number: NCT06358794

Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

Non-obstructive azoospermia (NOA) stands as the most severe form of male infertility. However, due to the diverse nature of testis focal spermatogenesis in NOA patients, accurately assessing the sperm retrieval rate (SRR) becomes challenging. The current study aims to develop and validate a noninvasive evaluation system based on machine learning, which can effectively estimate the SRR for NOA patients. In single-center investigation, NOA patients who underwent microdissection testicular sperm extraction (micro-TESE) were enrolled: (1) 2,438 patients from January 2016 to December 2022, and (2) 174 patients from January 2023 to May 2023 (as an additional validation cohort). The clinical features of participants were used to train, test and validate the machine learning models. Various evaluation metrics including area under the ROC (AUC), accuracy, etc. were used to evaluate the predictive performance of 8 machine learning models.

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Key information

Conditions

Infertility, Male Azoospermia, Nonobstructive Genital Diseases Genital Diseases, Male Infertility Male Urogenital Diseases Urogenital Diseases

Age range

20 year–60 year

Sex eligibility

Male

Study type

Observational

Primary location

Peking University Third Hospital

Beijing, Beijing Municipality, 100191, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • diagnosed with nonobstructive azoospermia
  • underwent microdissection testicular sperm extraction

Exclusion criteria

  • without intact clinical information
  • low data quality

Treatment and study plan

Machine learning-based predictive model

Diagnostic Test

The clinical features of participants were used to train, test and validate the machine learning models. Various evaluation metrics including area under the ROC (AUC), accuracy, etc. were used to evaluate the predictive performance of 8 machine learning models.

Primary outcomes

  1. SRR of micro-TESE

    Time frame: At the time after microdissection testicular sperm extraction

    the sperm retrieval success rate of microdissection testicular sperm extraction

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Registry information

Official study title

SpermFinder: Machine Learning Based-Personalized Prediction of Sperm Retrieval in Patients With Nonobstructive Azoospermia Prior to Microdissection Testicular Sperm Extraction

Important dates

Study start
2022
Primary completion
2022
Study completion
2023
First posted
Apr 11, 2024
Registry last updated
Apr 11, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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