AIMC Topic: Time-Lapse Imaging
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Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos.
Reproductive biomedicine online
Aug 13, 2022
RESEARCH QUESTION: What is the association between the deep learning-based scoring system, iDAScore, and biological events during the pre-implantation period?
Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?
Reproductive biomedicine online
Jun 17, 2022
RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data i...
Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.
Scientific reports
May 20, 2022
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from gene...
Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.
Computer methods and programs in biomedicine
May 16, 2022
BACKGROUND: Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditiona...
Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos.
IEEE transactions on medical imaging
Feb 2, 2022
With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility trea...
Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences.
PloS one
Feb 2, 2022
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep le...
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.
PLoS computational biology
Jan 18, 2022
Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within ti...
Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.
BMC pregnancy and childbirth
Jan 16, 2022
BACKGROUND: Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the ...
Time-Lapse Systems: A Comprehensive Analysis on Effectiveness.
Seminars in reproductive medicine
Jan 10, 2022
Time-lapse systems have quickly become a common feature of in vitro fertilization laboratories all over the world. Since being introduced over a decade ago, the alleged benefits of time-lapse technology have continued to grow, from undisturbed cultur...
Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.
PLoS computational biology
Dec 30, 2021
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static...
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