Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

NCT06358794 | Completed

• 1 other identifier: IRB00006761-M2022692
• Study Type: observational
• Target: 2,612
• Locations: 1 country
• Sites: 1

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.

Conditions

Infertility, Male; Azoospermia, Nonobstructive

Keywords

Machine learning; Predictive model; Sperm retrieval

Outcome Measures

Primary Outcomes (1)

• SRR of micro-TESE

  the sperm retrieval success rate of microdissection testicular sperm extraction

  At the time after microdissection testicular sperm extraction

Study Arms (2)

Training cohort

2,438 patients diagnosed with NOA were included for model training and validation

Diagnostic Test: Machine learning-based predictive model

External validation cohort

174 participants from January 2023 to May 2023 were included as the external validation cohort for online platform

Interventions

Machine learning-based predictive model DIAGNOSTIC_TEST

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.

Training cohort

Eligibility Criteria

• Age: 20 Years - 60 Years
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Nonobstructive azoospermia patients who underwent microdissection testicular sperm extraction at the Reproductive Center of Peking University Third Hospital were respectively enrolled.

You may qualify if:

• diagnosed with nonobstructive azoospermia
• underwent microdissection testicular sperm extraction

You may not qualify if:

• without intact clinical information
• low data quality

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (1)

Peking University Third Hospital

Beijing, Beijing Municipality, 100191, China

Location

MeSH Terms

Conditions

Infertility, Male; Azoospermia, Nonobstructive

Condition Hierarchy (Ancestors)

Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Infertility; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 7, 2024
• First Posted: April 11, 2024
• Study Start: June 1, 2022
• Primary Completion: December 31, 2022
• Study Completion: May 31, 2023
• Last Updated: April 11, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)