Machine Learning Based-Personalized Prediction of Sperm Retrieval Success Rate
NCT ID: NCT06358794
Last Updated: 2024-04-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
2612 participants
OBSERVATIONAL
2022-06-01
2023-05-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Training cohort
2,438 patients diagnosed with NOA were included for model training and validation
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.
External validation cohort
174 participants from January 2023 to May 2023 were included as the external validation cohort for online platform
No interventions assigned to this group
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* underwent microdissection testicular sperm extraction
Exclusion Criteria
* low data quality
20 Years
60 Years
MALE
No
Sponsors
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Peking University Third Hospital
OTHER
Responsible Party
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Locations
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Peking University Third Hospital
Beijing, Beijing Municipality, China
Countries
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Other Identifiers
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IRB00006761-M2022692
Identifier Type: -
Identifier Source: org_study_id
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