ClinicalTrials.gg
CompletedNCT06358794Updated Apr 11, 2024

Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate

An observational study in Infertility, Male and Azoospermia, Nonobstructive, sponsored by Peking University Third Hospital. Completed at 1 site in China. Open to male participants aged 20 Years to 60 Years. Per ClinicalTrials.gov, last updated 2024-04-11.

Primary conditionAzoospermia, NonobstructiveRegistry: Infertility, MaleReclassifiedWhy we changed it
Registry
Infertility, Male — the first condition term the registry derived for this study.
ClinicalTrials.gg
Azoospermia, Nonobstructive
Why
  1. The study's listed conditions name Azoospermia, Nonobstructive, a narrower term within Infertility, Male.
Confidence
High: the new condition is named in the study's own listed conditions. Of 351 hand-checked studies reclassified this way, 68% were exactly right and 84% in the right disease family; the registry's first term was right for 5% (measured 2026-10-21). How we classify →
Wrong?
Tell us the study's main condition →

Sponsored by Peking University Third Hospital · Observational

Study type
Observational
Model
Cohort
Time perspective
Retrospective
Enrollment
2,612
Ages
20 Years to 60 Years
Sex
Male
01

Study 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.

02

Conditions studied

  • Infertility, Male
  • Azoospermia, Nonobstructive

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Keywords

  • Machine learning
  • Predictive model
  • Sperm retrieval
03

In context

Azoospermia, Nonobstructive Reclassified

23 studies on the registry are indexed under Azoospermia, Nonobstructive; 7 are open to participants now.

This study's enrollment of 2,612 is above the median of 94 across 10 observational studies indexed under Azoospermia, Nonobstructive.

Browse Azoospermia, Nonobstructive studies →

Lead sponsor

Peking University Third Hospital is the lead sponsor of 752 studies on the registry; 273 are open to participants now.

Compared within Azoospermia, Nonobstructive, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Infertility, Male. The counts are of registry records indexed under each term, refreshed daily. How we classify →

04

Who can participate

Ages eligible
20 Years to 60 Years
Sexes eligible
Male
Accepts healthy volunteers
No
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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • diagnosed with nonobstructive azoospermia
  • underwent microdissection testicular sperm extraction

Exclusion criteria

Exclusion Criteria:

  • without intact clinical information
  • low data quality
05

Study design

Observational model
Cohort
Time perspective
Retrospective
Enrollment
2,612 participants (actual)
Patient registry
No

Groups and cohorts

  • 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

  • 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.

06

What researchers measure

Primary outcomes

  1. SRR of micro-TESE

    the sperm retrieval success rate of microdissection testicular sperm extraction

    Time frame: At the time after microdissection testicular sperm extraction

07

Study locations

1 site
  • Peking University Third Hospital
    Beijing, Beijing 100191, China
08

References and documents

Individual participant data

Plan to share: No — The raw clinical data are not publicly available. Processed nonsensitive data and analysis code are available from the corresponding author on reasonable request.

No publications or documents are linked to this record.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Ran from Jun 2022 to primary completion in Dec 2022, 6 months.

How these figures are worked out, and which studies are left out of them.

Registered
Apr 7, 2024
First posted
Apr 11, 2024
StartActual
Jun 1, 2022
Primary completionActual
Dec 31, 2022
CompletionActual
May 31, 2023
Last update
Apr 11, 2024
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Apr 11, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06358794
Responsible party
Sponsor
First posted
Apr 11, 2024
Start date
Jun 1, 2022
Primary completion
Dec 31, 2022
Completion
May 31, 2023
Last update
Apr 11, 2024

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Apr 2024. You cannot join it, but the record below documents what was studied.

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