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.

Inclusion Criteria:

• diagnosed with nonobstructive azoospermia
• underwent microdissection testicular sperm extraction

Exclusion Criteria:

• without intact clinical information
• low data quality

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

The raw clinical data are not publicly available. Processed nonsensitive data and analysis code are available from the corresponding author on reasonable request.