Reply: Predicting sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective

Affiliations

¹ IVF Unit, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev Yitzhack I. Rager Blvd 151, POB 151, Beer Sheva 8457108, Israel.

Comment

A Zeadna et al. Hum Reprod. 2020.

. 2020 Dec 1;35(12):2873-2876.

doi: 10.1093/humrep/deaa259.

¹ IVF Unit, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev Yitzhack I. Rager Blvd 151, POB 151, Beer Sheva 8457108, Israel.

No abstract available

Comment on

Prediction of sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective.
Zeadna A, Khateeb N, Rokach L, Lior Y, Har-Vardi I, Harlev A, Huleihel M, Lunenfeld E, Levitas E. Zeadna A, et al. Hum Reprod. 2020 Jul 1;35(7):1505-1514. doi: 10.1093/humrep/deaa109. Hum Reprod. 2020. PMID: 32538428
Predicting sperm extraction in non-obstructive azoospermia patients.
Boitrelle F, Bendayan M, Robin G. Boitrelle F, et al. Hum Reprod. 2020 Dec 1;35(12):2871-2872. doi: 10.1093/humrep/deaa258. Hum Reprod. 2020. PMID: 33167009 No abstract available.
Prediction of sperm retrieval with the aid of machine-learning models cannot help in the management of patients with non-obstructive azoospermia when a less-effective surgical treatment is used.
Caroppo E, Colpi GM. Caroppo E, et al. Hum Reprod. 2020 Dec 1;35(12):2872-2873. doi: 10.1093/humrep/deaa260. Hum Reprod. 2020. PMID: 33167038 No abstract available.