Ignite Creation Date:
2024-05-06 @ 8:21 PM
Last Modification Date:
2024-10-26 @ 3:26 PM
Study NCT ID:
NCT06358794
Status:
COMPLETED
Last Update Posted:
2024-04-11
First Post:
2024-04-07
Brief Title:
Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate
Sponsor:
Peking University Third Hospital
Organization:
Peking University Third Hospital
Study Overview
Official Title:
SpermFinder Machine Learning Based-Personalized Prediction of Sperm Retrieval in Patients With Nonobstructive Azoospermia Prior to Microdissection Testicular Sperm Extraction
Status:
COMPLETED
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 2438 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:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None