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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06358794

Enrollment

2612

Registered

2024-04-11

Start date

2022-06-01

Completion date

2023-05-31

Last updated

2024-04-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Azoospermia, Nonobstructive, Infertility, Male

Keywords

Machine learning, Predictive model, Sperm retrieval

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.

Interventions

DIAGNOSTIC_TESTMachine 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.

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

MALE

Age

20 Years to 60 Years

Healthy volunteers

Inclusion criteria

* diagnosed with nonobstructive azoospermia * underwent microdissection testicular sperm extraction

Exclusion criteria

* without intact clinical information * low data quality

Design outcomes

Primary

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

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026