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Authors
Wang, Jiaqi
Jin, Yufei
Jiang, Aojun
Chen, Wenyuan
Shan, Guanqiao
Gu, Yifan
Ming, Yue
Li, Jichang
Yue, Chunfeng
Huang, Zongjie
Librach, Clifford
Lin, Ge
Wang, Xibu
Zhao, Huan
Sun, Yu
Zhang, Zhuoran
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Funding
Funding for this research was provided by:
National Key Research and Development Program of China (2023YFE0205500)
National Natural Science Foundation of China (62203374)
Guangdong Basic and Applied Basic Research Foundation (2021A1515110023)
Shenzhen Science and Technology Innovation Program (RCBS20210706092254072)
Chinese University of Hong Kong, Shenzhen (UDF01002141)
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Article History
Received: 3 March 2024
Accepted: 14 May 2024
First Online: 22 May 2024
Declarations
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: Our study strictly adheres to the ethical principles for medical research involving human subjects, as outlined in the Declaration of Helsinki by the World Medical Association. The study protocol has been reviewed and approved by the respective ethics committees of the participating clinics. This study was tested among three clinics, including 1) The 3rd Affiliated Hospital of Shenzhen University in Shenzhen, China, with IRB approval number: 2021-LHRMYY-SZLL-012; 2) Reproductive & Genetic Hospital of Citic-Xiangya in Changsha, China, with IRB approval number: LL-SC2021-016; and 3) CReATe Fertility Centre in Toronto, Canada, with IRB approval number: UT35544. We have taken all necessary measures to protect the rights and privacy of the participants. It is worth noting that this study is not a clinical trial because the study only involves the use of human sperm samples, without any intervention to patients or evaluating any outcomes on human health. With patients’ consents, this study acquired and analyzed images of human sperm samples. The images were then used for evaluating the generalizability of deep learning models for sperm detection.
: Not applicable.
: The authors declare no competing interests.
