A deep learning model using time-lapse imaging data to predict successful clinical pregnancy and embryo euploidy

被引:0
|
作者
Maekawa, R. [1 ]
Tanaka, A. [2 ]
Kuramoto, T. [3 ]
Sugino, N. [1 ]
机构
[1] Yamaguchi Univ, Sch Med, Dept Obstet & Gynecol, Yamaguchi, Japan
[2] St Mother Hosp, Infertil Clin, Kitakyushu, Fukuoka, Japan
[3] Kuramoto Womens Clin, Infertil Clin, Fukuoka, Japan
关键词
D O I
暂无
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
P-137
引用
收藏
页码:I257 / I257
页数:1
相关论文
共 50 条
  • [41] End-to-end deep learning for recognition of ploidy status using time-lapse videos
    Lee, C. I.
    Su, Y. R.
    Chen, C. H.
    Chang, T. A.
    Kuo, E. E. S.
    Hsieh, W. T.
    Huang, C. C.
    Lee, M. S.
    Liu, M.
    HUMAN REPRODUCTION, 2021, 36 : 9 - 9
  • [42] Can novel early non-invasive biomarkers of embryo quality be identified with time-lapse imaging to predict live birth?
    Barberet, J.
    Bruno, C.
    Valot, E.
    Antunes-Nunes, C.
    Jonval, L.
    Chammas, J.
    Choux, C.
    Ginod, P.
    Sagot, P.
    Soudry-Faure, A.
    Fauque, P.
    HUMAN REPRODUCTION, 2019, 34 (08) : 1439 - 1449
  • [43] Reply: Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer
    Tran, D.
    Cooke, S.
    Illingworth, P. J.
    Gardner, D. K.
    HUMAN REPRODUCTION, 2020, 35 (02) : 483 - 483
  • [44] CO2 storage monitoring based on time-lapse seismic data via deep learning
    Li, Dong
    Peng, Suping
    Guo, Yinling
    Lu, Yongxu
    Cui, Xiaoqin
    INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL, 2021, 108
  • [45] A new artificial intelligence (AI) system in the block: impact of clinical data on embryo selection using four different time-lapse incubators
    Delestro, F.
    Nogueira, D.
    Ferrer-Buitrago, M.
    Boyer, P.
    Chansel-Debordeaux, L.
    Keppi, B.
    Sanguinet, P.
    Trebesses, L.
    Scalici, E.
    De La Fuente, A.
    Gomez, E.
    Pollet-Villard, X.
    Ruiz-Jorro, M.
    Boussommier-Calleja, A.
    HUMAN REPRODUCTION, 2022, 37
  • [46] Morphokinetics of early equine embryo development in vitro using time-lapse imaging, and use in selecting blastocysts for transfer
    Lewis, Niamh
    Schnauffer, Karen
    Hinrichs, Katrin
    Morganti, Monica
    Troup, Stephen
    Argo, Caroline
    REPRODUCTION FERTILITY AND DEVELOPMENT, 2019, 31 (12) : 1851 - 1861
  • [47] Embryo quality, blastocyst and ongoing pregnancy rates in oocyte donation patients whose embryos were monitored by time-lapse imaging
    María Cruz
    Blanca Gadea
    Nicolás Garrido
    Kamilla Søe Pedersen
    Mar Martínez
    Inma Pérez-Cano
    Manuel Muñoz
    Marcos Meseguer
    Journal of Assisted Reproduction and Genetics, 2011, 28 : 569 - 573
  • [48] Does one model fit all? Testing a published embryo selection algorithm on independent time-lapse data
    Best, L.
    Campbell, A.
    Duffy, S.
    Montgomery, S.
    Fishel, S.
    HUMAN REPRODUCTION, 2013, 28 : 88 - 89
  • [49] Embryo quality, blastocyst and ongoing pregnancy rates in oocyte donation patients whose embryos were monitored by time-lapse imaging
    Cruz, Maria
    Gadea, Blanca
    Garrido, Nicolas
    Pedersen, Kamilla Soe
    Martinez, Mar
    Perez-Cano, Inma
    Munoz, Manuel
    Meseguer, Marcos
    JOURNAL OF ASSISTED REPRODUCTION AND GENETICS, 2011, 28 (07) : 569 - 573
  • [50] Measuring water ponding time, location and connectivity on soil surfaces using time-lapse images and deep learning
    Zamboni, Pedro
    Bluemlein, Mikesch
    Lenz, Jonas
    Goncalves, Wesley Nunes
    Marcato Jr, Jose
    Woehling, Thomas
    Eltner, Anette
    CATENA, 2025, 254