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Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

Infertility, Male Clinical Trial

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

Clinical Trial 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.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06358794
Study type Observational
Source Peking University Third Hospital
Contact
Status Completed
Phase
Start date June 1, 2022
Completion date May 31, 2023
View Details
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