AIMC Topic: Blastocyst
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Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development.
Reproductive biomedicine online
Dec 24, 2020
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?
An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study.
Reproductive biomedicine online
Oct 8, 2020
RESEARCH QUESTION: The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology...
Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
eLife
Sep 15, 2020
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistiv...
Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential.
Fertility and sterility
Sep 8, 2020
OBJECTIVE: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.
Humans
Neural Networks, Computer
Time Factors
Pregnancy Rate
Fertilization in Vitro
Treatment Outcome
Retrospective Studies
Pregnancy
Female
Infertility
Single Embryo Transfer
Fertility
Oocyte Donation
Image Interpretation, Computer-Assisted
Time-Lapse Imaging
Embryo Implantation
Predictive Value of Tests
Blastocyst
Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.
Lab on a chip
Nov 22, 2019
Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly sub...
Automatic grading of human blastocysts from time-lapse imaging.
Computers in biology and medicine
Oct 15, 2019
BACKGROUND: Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges,...
Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018.
Journal of assisted reproduction and genetics
Jan 28, 2019
Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every as...
Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens.
Sensors (Basel, Switzerland)
Dec 15, 2018
In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers ...
Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.
Journal of assisted reproduction and genetics
Jul 27, 2018
Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rate...
A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.
Scientific reports
Aug 9, 2017
Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method ...
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