{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:03:28Z","timestamp":1792109008697,"version":"4.4.0"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071698"],"award-info":[{"award-number":["82071698"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271634"],"award-info":[{"award-number":["82271634"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271630"],"award-info":[{"award-number":["82271630"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Andrology"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Non\u2010obstructive azoospermia represents the most severe form of male infertility. The heterogeneous nature of focal spermatogenesis within the testes of non\u2010obstructive azoospermia patients poses significant challenges for accurately predicting sperm retrieval rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop a machine learning\u2013based predictive model for estimating sperm retrieval rates in patients with non\u2010obstructive azoospermia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This multi\u2010center study included more than 2800 men with non\u2010obstructive azoospermia who underwent microdissection testicular sperm extraction. Preoperative clinical variables were used to train, test, and validate multiple machine learning models. The predictive performance of eight models was assessed with several metrics, including area under the receiver operating characteristic curve, overall accuracy, etc.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the eight models evaluated, Extreme Gradient Boosting, Random Forest, and Light Gradient Boosting Machine consistently outperformed the others. Extreme Gradient Boosting, which achieved the highest mean area under the receiver operating characteristic curve (0.9183), was selected to power SpermFinder\u2014an online calculator for sperm retrieval rates prediction. The model maintained strong discriminatory ability in both validation sets, with an area under the receiver operating characteristic curve of 0.8469 in the internal cohort and 0.8301 in the external cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and conclusion<\/jats:title>\n                    <jats:p>By leveraging routine clinical features and machine learning\u2013powered models, we developed a web\u2010based platform that reliably predicts sperm retrieval outcomes in men with non\u2010obstructive azoospermia. The predictive tool could provide valuable insights for preoperative assessments, and patients with a lower probability of success could gain the opportunity to make informed decisions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/andr.70114","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T07:31:10Z","timestamp":1757316670000},"page":"237-246","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine learning\u2013based personalized prediction of sperm retrieval in patients with non\u2010obstructive azoospermia prior to microdissection testicular sperm extraction: A multi\u2010center cohort study"],"prefix":"10.1111","volume":"14","author":[{"given":"Yu","family":"Xi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Female Fertility Promotion Center for Reproductive Medicine Department of Urology Peking University Third Hospital  Beijing 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