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 Details — Status: Completed

Administrative data

• NCT number: NCT06358794
• Other study ID #: IRB00006761-M2022692
• Status: Completed
• Start date: June 1, 2022
• Est. completion date: May 31, 2023

Study information

• Verified date: April 2024
• Source: Peking University Third Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 2612
• Est. completion date: May 31, 2023
• Est. primary completion date: December 31, 2022
• Accepts healthy volunteers: No
• Gender: Male
• Age group: 20 Years to 60 Years

Eligibility

Inclusion Criteria:

• diagnosed with nonobstructive azoospermia

• underwent microdissection testicular sperm extraction

Exclusion Criteria:

• without intact clinical information

• low data quality

Related Conditions & MeSH terms

• Azoospermia
• Azoospermia, Nonobstructive
• Infertility
• Infertility, Male

Intervention

Diagnostic Test:
• Machine learning-based predictive model
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.

Locations

Country	Name	City	State
China	Peking University Third Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Peking University Third Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	SRR of micro-TESE	the sperm retrieval success rate of microdissection testicular sperm extraction	At the time after microdissection testicular sperm extraction