Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction

Jiang, Victoria S.; Bormann, Charles L.

doi:10.1016/j.fertnstert.2023.06.025

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…Morphokinetics has significant applications in human in vitro fertilization (IVF) clinics [ 1 , 2 ], particularly in selecting viable embryos for implantation, possibly supplementing preimplantation genetic testing and improving pregnancy [ 3–6 ]. Changes in embryo morphokinetics are linked to many phenotypes including blastocyst formation [ 7–10 ], aneuploidy status [ 11–18 ], ovarian disease [ 19–21 ], and pregnancy rates [ 4 , 22–34 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice

Yang,

Leynes,

Pawelka

et al. 2024

Biology of Reproduction

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Time-lapse microscopy for embryos is a non-invasive technology used to characterize early embryo development. This study employs time-lapse microscopy and machine learning to elucidate changes in embryonic growth kinetics with maternal aging. We analyzed morphokinetic parameters of embryos from young and aged C57BL6/NJ mice via continuous imaging. Our findings show that aged embryos accelerated through cleavage stages (from 5-cells) to morula compared to younger counterparts, with no significant differences observed in later stages of blastulation. Unsupervised machine learning identified two distinct clusters comprising of embryos from aged or young donors. Moreover, in supervised learning, the XGBoost (extreme gradient boosting) algorithm successfully predicted the age-related phenotype with 0.78 accuracy, 0.81 precision, and 0.83 recall following hyperparameter tuning. These results highlight two main scientific insights: maternal aging affects embryonic development pace, and that AI can differentiate between embryos from aged and young maternal mice by a non-invasive approach. Thus, machine learning can be used to identify morphokinetics phenotypes for further studies. This study has potential for future applications in selecting human embryos for embryo transfer, without or in complement with preimplantation genetic testing.

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Section: Introductionmentioning

confidence: 99%

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice

Yang,

Leynes,

Pawelka

et al. 2024

Biology of Reproduction

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“…Arguably the most cutting-edge research associated with embryo time-lapse imaging is the application of artificial intelligence (AI) for embryo screening. Using AI as a means of non-invasive embryo screening is a promising area of research yet there is still more to learn before we will see AI routinely used in the clinic (reviewed by Jiang and Bormann, 2023 ).…”

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confidence: 99%

Editorial: Molecular and cellular mechanisms in preimplantation IVF-embryo development

Chosed,

Kavoussi,

Berger

et al. 2023

Front. Cell Dev. Biol.

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Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

Shahali,

Murshed,

Spencer

et al. 2024

Advanced Intelligent Systems

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Sperm morphology analysis is crucial in infertility diagnosis and treatment. However, current clinical analytical methods use either chemical stains that render cells unusable for treatment or rely on subjective manual inspection. Here, an ensemble deep‐learning model is presented for classification of live, unstained human sperm using whole‐cell morphology. This model achieves an accuracy and precision of 94% benchmarked against the consensus of three andrology scientists who classified the images independently. The model loses less than a 12% prediction performance even when image resolution is reduced by over sixfold. This ensures compatibility across varied clinical imaging setups. This model also provides a high certainty and robust classification of challenging images, which divided the experts. By providing a consistent, automated approach for classifying live, unstained cells using quantitative data, this model offers promising future opportunities for enhancing clinical sperm selection practices and reducing day‐to‐day variability in clinics.

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Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction

Cited by 13 publications

References 17 publications

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice

Editorial: Molecular and cellular mechanisms in preimplantation IVF-embryo development

Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

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