Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation

This publication has 22 references indexed in Scilit:

• Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth
  Reproductive BioMedicine Online, 2021
• Empathetic application of machine learning may address appropriate utilization of ART
  Reproductive BioMedicine Online, 2020
• Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning
  Advanced Intelligent Systems, 2020
• A stepwise approach to move from a cleavage-stage to a blastocyst-stage transfer policy for all patients in the IVF clinic
  Human Reproduction Open, 2020
• Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer
  Human Reproduction, 2019
• Direct Unequal Cleavages: Embryo Developmental Competence, Genetic Constitution and Clinical Outcome
  PLOS ONE, 2016
• Cumulative live birth rates after fresh and vitrified cleavage-stage versus blastocyst-stage embryo transfer in the first treatment cycle
  Human Reproduction, 2016
• Which set of embryo variables is most predictive for live birth? A prospective study in 6252 single embryo transfers to construct an embryo score for the ranking and selection of embryos
  Human Reproduction, 2014
• National collection of embryo morphology data into Society for Assisted Reproductive Technology Clinic Outcomes Reporting System: associations among day 3 cell number, fragmentation and blastomere asymmetry, and live birth rate
  Fertility and Sterility, 2011
• The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting
  Human Reproduction, 2011