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Embryonic Development

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Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study.

Applied microbiology and biotechnology
Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three i...

Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development.

Reproductive biomedicine online
RESEARCH QUESTION: Can artificial intelligence and advanced image analysis extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst?

Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.

Nature communications
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell...

Application of convolutional neural network on early human embryo segmentation during in vitro fertilization.

Journal of cellular and molecular medicine
Selection of the best quality embryo is the key for a faithful implantation in in vitro fertilization (IVF) practice. However, the process of evaluating numerous images captured by time-lapse imaging (TLI) system is time-consuming and some important ...

Towards the selection of embryos with the greatest implantation potential.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
Choosing the most suitable embryo remains challenging as the standard approach to select top-quality embryos for transfer rely on static morphological assessment. It is completed after fertilisation, on days 3 and 5 post oocyte retrieval and evaluate...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...

A machine learning system with reinforcement capacity for predicting the fate of an ART embryo.

Systems biology in reproductive medicine
The aim of this work was o construct a score issued from a machine learning system with self-improvement capacity able to predict the fate of an ART embryo incubated in a time lapse monitoring (TLM) system. A retrospective study was performed. For th...

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring.

Communications biology
Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we...

Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.

Journal of assisted reproduction and genetics
Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture co...

Deep learning is widely applicable to phenotyping embryonic development and disease.

Development (Cambridge, England)
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...