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Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.

作者信息

Chavez-Badiola A, Mendizabal-Ruiz G, Flores-Saiffe Farias A, Garcia-Sanchez R, Drakeley Andrew J

机构信息

Computational Biology, New Hope Fertility Center Mexico, Guadalajara, Mexico.

Research and Development, Darwin Technologies Ltd, Liverpool, UK.

出版信息

Hum Reprod. 2020 Feb 29;35(2):482. doi: 10.1093/humrep/dez263.

DOI:10.1093/humrep/dez263
PMID:32053171
Abstract
摘要

相似文献

1
Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.深度学习作为延时培养和囊胚移植后胎儿心脏妊娠的预测工具。
Hum Reprod. 2020 Feb 29;35(2):482. doi: 10.1093/humrep/dez263.
2
Reply: Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.回复：深度学习作为延时培养和囊胚移植后胎儿心脏妊娠的预测工具。
Hum Reprod. 2020 Feb 29;35(2):483. doi: 10.1093/humrep/dez264.
3
Can deep learning automatically predict fetal heart pregnancy with almost perfect accuracy?深度学习能否以近乎完美的准确性自动预测胎儿心脏妊娠情况？
Hum Reprod. 2020 Jun 1;35(6):1473. doi: 10.1093/humrep/deaa083.
4
Reply: Can deep learning automatically predict fetal heart pregnancy with almost perfect accuracy?回复：深度学习能否以几乎完美的准确率自动预测胎儿心脏妊娠情况？
Hum Reprod. 2020 Jun 1;35(6):1474. doi: 10.1093/humrep/deaa084.
5
Comparison of the pregnancy and obstetric outcomes between single cleavage-stage embryo transfer and single blastocyst transfer by time-lapse selection of embryos.比较胚胎延时选择法进行的单个卵裂期胚胎移植和单个囊胚移植的妊娠和产科结局。
Gynecol Endocrinol. 2019 Sep;35(9):792-795. doi: 10.1080/09513590.2019.1594762. Epub 2019 Apr 11.
6
Time lapse microscopy is useful for elective single-embryo transfer.延时显微镜检查法对于选择性单胚胎移植很有用。
Gynecol Endocrinol. 2016 Oct;32(10):816-818. doi: 10.1080/09513590.2016.1188375. Epub 2016 May 26.
7
Single blastocyst embryo transfer maintains comparable pregnancy rates to double cleavage-stage embryo transfer but results in healthier pregnancy outcomes.单囊胚胚胎移植与双卵裂期胚胎移植的妊娠率相当，但妊娠结局更健康。
Aust N Z J Obstet Gynaecol. 2011 Oct;51(5):406-10. doi: 10.1111/j.1479-828X.2011.01324.x. Epub 2011 Jun 9.
8
Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: a randomized, open-label, non-inferiority trial.第三天的时间延迟选择与第五天的传统形态学选择进行单胚胎移植：一项随机、开放标签、非劣效性试验。
Hum Reprod. 2018 May 1;33(5):869-876. doi: 10.1093/humrep/dey047.
9
Pregnancy achieved by transfer of a single blastocyst selected by time-lapse monitoring.通过延时监测选择单个囊胚进行移植而实现妊娠。
Reprod Biomed Online. 2010 Oct;21(4):533-6. doi: 10.1016/j.rbmo.2010.04.015. Epub 2010 Apr 24.
10
Time-lapse imaging of inner cell mass splitting with monochorionic triamniotic triplets after elective single embryo transfer: a case report.选择性单胚胎移植后联体三羊膜囊三胎中内细胞团分裂的延时成像：一例报告。
Reprod Biomed Online. 2019 Apr;38(4):491-496. doi: 10.1016/j.rbmo.2018.12.017. Epub 2018 Dec 21.

引用本文的文献

1
Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.使用延时成像技术进行人类胚胎评估的深度学习应用：范围综述
Front Reprod Health. 2025 Apr 8;7:1549642. doi: 10.3389/frph.2025.1549642. eCollection 2025.
2
A brief history of artificial intelligence embryo selection: from black-box to glass-box.人工智能胚胎选择的简史：从黑箱到玻璃箱。
Hum Reprod. 2024 Feb 1;39(2):285-292. doi: 10.1093/humrep/dead254.
3
A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems.
一种混合人工智能模型利用多中心临床数据，改善跨时间 lapse 系统的胎儿心率妊娠预测。
Hum Reprod. 2023 Apr 3;38(4):596-608. doi: 10.1093/humrep/dead023.
4
Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles.机器学习在预测体外受精周期胚胎非整倍体和嵌合体中的应用。
AJOG Glob Rep. 2022 Sep 19;2(4):100103. doi: 10.1016/j.xagr.2022.100103. eCollection 2022 Nov.
5
Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.基于人工智能和延时图像序列的稳健且可推广的胚胎选择。
PLoS One. 2022 Feb 2;17(2):e0262661. doi: 10.1371/journal.pone.0262661. eCollection 2022.
6
Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.利用深度学习技术，从超过 10000 个胚胎数据中预测活产结局。
BMC Pregnancy Childbirth. 2022 Jan 16;22(1):36. doi: 10.1186/s12884-021-04373-5.