Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study-Reference-Cited by-同舟云学术

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study

Published:2022-08-13 Issue:10 Volume:39 Page:2343-2348
ISSN:1058-0468
Container-title:Journal of Assisted Reproduction and Genetics
language:en
Short-container-title:J Assist Reprod Genet

Author:

Hammer Karissa C.^ORCID,Jiang Victoria S.,Kanakasabapathy Manoj Kumar^ORCID,Thirumalaraju Prudhvi^ORCID,Kandula Hemanth^ORCID,Dimitriadis Irene,Souter Irene^ORCID,Bormann Charles L.^ORCID,Shafiee Hadi^ORCID

Funder

National Institute of Health

Brigham Precision Medicine Program

Brigham and Women's Hospital

Partners Healthcare

Publisher

Springer Science and Business Media LLC

Subject

Genetics (clinical),Developmental Biology,Obstetrics and Gynecology,Genetics,Reproductive Medicine,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10815-022-02585-y.pdf

Reference27 articles.

1. To err is human. January 10, 2021]; Available from: https://www.merriam-webster.com/dictionary/to%20err%20is%20human.

2. de los Santos, M.J. and A. Ruiz, Protocols for tracking and witnessing samples and patients in assisted reproductive technology. Fertil Steril, 2013. 100(6): p. 1499–502.

3. Letterie G. Outcomes of medical malpractice claims in assisted reproductive technology over a 10-year period from a single carrier. J Assist Reprod Genet. 2017;34(4):459–63.

4. Rasouli MA, Moutos CP, Phelps JY. Liability for embryo mix-ups in fertility practices in the USA. J Assist Reprod Genet. 2021;38(5):1101–7.

5. Sakkas D, Pool TB, Barrett CB. Analyzing IVF laboratory error rates: highlight or hide? Reprod Biomed Online. 2015;31(4):447–8.

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning;Advanced Intelligent Systems;2024-07-08

2. WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF;Journal of Assisted Reproduction and Genetics;2024-03-12

3. Human Embryo Zona Pellucida Microsurgery with Femtosecond Laser Pulses;2024

4. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies;Obstetrics and Gynecology Clinics of North America;2023-12

5. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade;Fertility and Sterility;2023-07