Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

Completed

• Conditions: Infertility, Male; Azoospermia, Nonobstructive
• Interventions: Diagnostic Test: Machine learning-based predictive model

• 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

Study Timeline

• Study Start: 2022-06-01
• Primary Completion: 2022-12-31
• Study Completion: 2023-05-31
• Status Verified: 2024-04
• Study First Submitted: 2024-04-07
• Study First Submitted QC: 2024-04-07
• Last Update Submitted: 2024-04-07
• Study First Posted: 2024-04-11
• Last Update Posted: 2024-04-11

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Male
• Target Recruitment: 2612

Inclusion Criteria

• diagnosed with nonobstructive azoospermia
• underwent microdissection testicular sperm extraction

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Exclusion Criteria

• without intact clinical information
• low data quality

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Training cohort	Machine learning-based predictive model	2,438 patients diagnosed with NOA were included for model training and validation

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

Trial Locations

Locations (1)

Peking University Third Hospital; 🇨🇳 Beijing, Beijing, China