Clinical Trials Directory

Trials / Completed

CompletedNCT06358794

Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

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

Status
Completed
Phase
Study type
Observational
Enrollment
2,612 (actual)
Sponsor
Peking University Third Hospital · Academic / Other
Sex
Male
Age
20 Years – 60 Years
Healthy volunteers
Not accepted

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

Interventions

TypeNameDescription
DIAGNOSTIC_TESTMachine learning-based predictive modelThe 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.

Timeline

Start date
2022-06-01
Primary completion
2022-12-31
Completion
2023-05-31
First posted
2024-04-11
Last updated
2024-04-11

Locations

1 site across 1 country: China

Source: ClinicalTrials.gov record NCT06358794. Inclusion in this directory is not an endorsement.