AIMC Topic: Time-Lapse Imaging
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Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence.
Scientific reports
Jul 2, 2025
Oscillations are a common phenomenon in cell biology. They are based on non-linear coupling of biochemical reactions and can show rich dynamic behavior as found in, for example, glycolysis of yeast cells. Here, we show that dynamic mode decomposition...
Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.
Scientific reports
Apr 5, 2025
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual ce...
Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.
Reproductive biology and endocrinology : RB&E
Jan 31, 2025
BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency ...
A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.
Scientific reports
Nov 22, 2024
The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the l...
A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images.
Scientific reports
Nov 14, 2024
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer o...
Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.
eLife
Sep 23, 2024
Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requir...
Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?
Frontiers in endocrinology
Aug 29, 2024
PURPOSE: With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in ...
Deep learning large-scale drug discovery and repurposing.
Nature computational science
Aug 21, 2024
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...
A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.
Journal of ovarian research
Mar 15, 2024
BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to...
Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.
Journal of assisted reproduction and genetics
Oct 26, 2023
With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental chang...
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