A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

MLA

S. Canosa, et al. “A Novel Machine-Learning Framework Based on Early Embryo Morphokinetics Identifies a Feature Signature Associated with Blastocyst Development.” Journal of Ovarian Research, vol. 17, no. 1, Mar. 2024, pp. 1–11. EBSCOhost, https://doi.org/10.1186/s13048-024-01376-6.

APA

S. Canosa, N. Licheri, L. Bergandi, G. Gennarelli, C. Paschero, M. Beccuti, D. Cimadomo, G. Coticchio, L. Rienzi, C. Benedetto, F. Cordero, & A. Revelli. (2024). A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development. Journal of Ovarian Research, 17(1), 1–11. https://doi.org/10.1186/s13048-024-01376-6

Chicago

S. Canosa, N. Licheri, L. Bergandi, G. Gennarelli, C. Paschero, M. Beccuti, D. Cimadomo, et al. 2024. “A Novel Machine-Learning Framework Based on Early Embryo Morphokinetics Identifies a Feature Signature Associated with Blastocyst Development.” Journal of Ovarian Research 17 (1): 1–11. doi:10.1186/s13048-024-01376-6.