{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:05:24Z","timestamp":1792785924426,"version":"4.5.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Division of Obstetrics and Gynecology"},{"name":"Soroka University Medical Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>STUDY QUESTION<\/jats:title>\n                    <jats:p>Can a machine-learning-based model trained in clinical and biological variables support the prediction of the presence or absence of sperm in testicular biopsy in non-obstructive azoospermia (NOA) patients?<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>SUMMARY ANSWER<\/jats:title>\n                    <jats:p>Our machine-learning model was able to accurately predict (AUC of 0.8) the presence or absence of spermatozoa in patients with NOA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>WHAT IS KNOWN ALREADY<\/jats:title>\n                    <jats:p>Patients with NOA can conceive with their own biological gametes using ICSI in combination with successful testicular sperm extraction (TESE). Testicular sperm retrieval is successful in up to 50% of men with NOA. However, to the best of our knowledge, there is no existing model that can accurately predict the success of sperm retrieval in TESE. Moreover, machine-learning has never been used for this purpose.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>STUDY DESIGN, SIZE, DURATION<\/jats:title>\n                    <jats:p>A retrospective cohort study of 119 patients who underwent TESE in a single IVF unit between 1995 and 2017 was conducted. All patients with NOA who underwent TESE during their fertility treatments were included. The development of gradient-boosted trees (GBTs) aimed to predict the presence or absence of spermatozoa in patients with NOA. The accuracy of these GBTs was then compared to a similar multivariate logistic regression model (MvLRM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>PARTICIPANTS\/MATERIALS, SETTING, METHODS<\/jats:title>\n                    <jats:p>We employed univariate and multivariate binary logistic regression models to predict the probability of successful TESE using a dataset from a retrospective cohort. In addition, we examined various ensemble machine-learning models (GBT and random forest) and evaluated their predictive performance using the leave-one-out cross-validation procedure. A cutoff value for successful\/unsuccessful TESE was calculated with receiver operating characteristic (ROC) curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MAIN RESULTS AND THE ROLE OF CHANCE<\/jats:title>\n                    <jats:p>ROC analysis resulted in an AUC of 0.807\u2009\u00b1\u20090.032 (95% CI 0.743\u20130.871) for the proposed GBTs and 0.75\u2009\u00b1\u20090.052 (95% CI 0.65\u20130.85) for the MvLRM for the prediction of presence or absence of spermatozoa in patients with NOA. The GBT approach and the MvLRM yielded a sensitivity of 91% vs. 97%, respectively, but the GBT approach has a specificity of 51% compared with 25% for the MvLRM. A total of 78 (65.3%) men with NOA experienced successful TESE. FSH, LH, testosterone, semen volume, age, BMI, ethnicity and testicular size on clinical evaluation were included in these models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>LIMITATIONS, REASONS FOR CAUTION<\/jats:title>\n                    <jats:p>This study is a retrospective cohort study, with all the associated inherent biases of such studies. This model was used only for TESE, since micro-TESE is not performed at our center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>WIDER IMPLICATIONS OF THE FINDINGS<\/jats:title>\n                    <jats:p>Machine-learning models may lay the foundation for a decision support system for clinicians together with their NOA patients concerning TESE. The findings of this study should be confirmed with further larger and prospective studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>STUDY FUNDING\/COMPETING INTEREST(S)<\/jats:title>\n                    <jats:p>The study was funded by the Division of Obstetrics and Gynecology, Soroka University Medical Center, there are no potential conflicts of interest for all authors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/humrep\/deaa109","type":"journal-article","created":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T07:27:50Z","timestamp":1590564470000},"page":"1505-1514","source":"Crossref","is-referenced-by-count":70,"title":["Prediction of sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective"],"prefix":"10.1093","volume":"35","author":[{"given":"A","family":"Zeadna","sequence":"first","affiliation":[{"name":"IVF Unit, Division of Obstetrics and Gynecology, Faculty of 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