{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:07:33Z","timestamp":1789067253179,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Andrology"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The Klinefelter syndrome is a common genetic cause of male infertility, and testicular sperm extraction (TESE) enables sperm retrieval in a subset of affected patients. However, predicting TESE success remains challenging due to the heterogeneous clinical and endocrinological presentation of the Klinefelter syndrome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We aimed to develop and externally validate machine learning models to predict sperm retrieval outcomes in infertile patients with the Klinefelter syndrome using routinely available clinical, hormonal, and testicular parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This multicenter retrospective study included 470 infertile patients with the Klinefelter syndrome who underwent TESE between January 2021 and November 2025. Data from 12 centers (\n                      <jats:italic>n<\/jats:italic>\n                      = 307) constituted the internal dataset and were randomly split into training (80%) and test (20%) sets, while data from one independent center (\n                      <jats:italic>n<\/jats:italic>\n                      = 163) were used for external validation. Five supervised ML algorithms\u2014Random Forest, Decision Tree, AdaBoost, Gradient Boosting, and Extreme Gradient Boosting\u2014were developed using repeated stratified five\u2010fold cross\u2010validation with five repeats. Model performance was evaluated using accuracy, F1 score, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and Youden's\n                      <jats:italic>J<\/jats:italic>\n                      index. Model interpretability was assessed using Shapley additive explanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Across internal cross\u2010validation, all ensemble\u2010based models demonstrated high discriminative performance (mean AUROC &gt; 0.95). During external validation, performance declined across models; however, the Random Forest model achieved the highest accuracy (0.83), AUROC (0.95), and Youden's\n                      <jats:italic>J<\/jats:italic>\n                      index (0.73), indicating superior generalizability. SHAP analysis identified follicle\u2010stimulating hormone as the most influential predictor, followed by total testosterone, luteinizing hormone, and bilateral testicular volume. Higher FSH and LH levels were associated with reduced sperm retrieval probability, whereas higher testosterone levels and larger testicular volumes increased the likelihood of successful retrieval.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning models can accurately and interpretable predict sperm retrieval outcomes in patients with the Klinefelter syndrome. Among the evaluated algorithms, Random Forest demonstrated the most robust and clinically balanced performance during external validation. Integration of ML\u2010based prediction tools may support individualized counseling and decision\u2010making in the management of infertility in the Klinefelter syndrome.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/andr.70297","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:21:27Z","timestamp":1782933687000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning\u2010Based Prediction of Sperm Retrieval Outcomes in Patients With Klinefelter Syndrome: A Multicenter Study With External Validation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6657-6227","authenticated-orcid":false,"given":"Murat","family":"G\u00fcl","sequence":"first","affiliation":[{"name":"Department of Urology Sel\u00e7uk University School of Medicine  Konya Turkey"},{"name":"Department of Andrology Sel\u00e7uk University School of Medicine  Konya 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