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Cristina Hickman

Imperial College London

38

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433

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P–165 Using Artificial Intelligence to Classify Embryo Shape: An International Perspective

2021 Human Reproduction
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P–378 Using a machine learning tool (72% accuracy with 64% PPV) to predict multiple live birth when transferring multiple embryos, based on embryo specific data

2021 Human Reproduction
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P–267 Characterising a novel embryo grading system for 4-cell embryos including symmetry, fragmentation, cell configuration, cell contacts per cell, distance between cells, and cell adhesion strength

2021 Human Reproduction
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P–535 Machine learning classification algorithms can predict the ploidy status on day 5 and 6 with a 79% accuracy using evolutive morphological parameters and patient age

2021 Human Reproduction
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P–280 Changes in oolemma height during ICSI injection on day 0 is associated with day 5–6 blastocyst formation

2021 Human Reproduction
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Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures

2021 arXiv: Computer Vision and Pattern Recognition
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Aneuploidy rates and morphokinetic parameters of embryos cultured in distinct culture media: a sibling oocyte study.

2021 Human Reproduction
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WHICH FACTORS SHOULD BE USED TO DETERMINE HOW MANY EMBRYOS TO TRANSFER? ANALYSIS OF AGE, FRESH OR FROZEN EMBRYO STATUS, AND EMBRYO QUALITY

2021 Fertility and Sterility
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An artificial intelligence model based on the proteomic profile of euploid embryos and time-lapse images: a preliminary study

2020 Reproductive Biomedicine Online
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Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

2020 Fertility and Sterility
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