Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate
- Conditions
- Infertility, MaleAzoospermia, Nonobstructive
- Registration Number
- NCT06358794
- Lead Sponsor
- Peking University Third Hospital
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Male
- Target Recruitment
- 2612
- diagnosed with nonobstructive azoospermia
- underwent microdissection testicular sperm extraction
- without intact clinical information
- low data quality
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method SRR of micro-TESE At the time after microdissection testicular sperm extraction the sperm retrieval success rate of microdissection testicular sperm extraction
- Secondary Outcome Measures
Name Time Method
Related Research Topics
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Trial Locations
- Locations (1)
Peking University Third Hospital
🇨🇳Beijing, Beijing, China
Peking University Third Hospital🇨🇳Beijing, Beijing, China