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Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

NCT06358794 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2612

Last updated 2024-04-11

No results posted yet for this study

Summary

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.

Conditions

  • Infertility, Male
  • Azoospermia, Nonobstructive

Interventions

DIAGNOSTIC_TEST

Machine learning-based predictive model

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.

Sponsors & Collaborators

  • Peking University Third Hospital

    lead OTHER

Eligibility

Min Age
20 Years
Max Age
60 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-06-01
Primary Completion
2022-12-31
Completion
2023-05-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06358794 on ClinicalTrials.gov