Back to search

Article

Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

2021-07-15

Abstract excerpt

<h4>Background: </h4> The success rates of in vitro fertilization (IVF) treatment are limited by the aneuploidy of human embryos. Pre-implantation genetic testing for aneuploidy(PGT-A) is often used to select embryos with normal ploidy but requires invasive embryo biopsy. MethodsWe performed chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples and developed a noninvasive embry...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
245872ce-894a-55bc-a04e-e29b2c2d8424
DOI
10.21203/rs.3.rs-617438/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified EmbryosDOI 10.21203/rs.3.rs-617438/v1
Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A CyclesSemantic proximity
A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation studySemantic proximity
A Non-invasive Chromosome Screening Strategy for Prioritizing in vitro Fertilization Embryos for ImplantationSemantic proximity
Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy predictionSemantic proximity
Efficacy of non-invasive chromosome screening, preimplantation genetic testing for aneuploidy, and morphological grading in selecting embryos of patients with advanced maternal age: a three-armed prospective cohort studySemantic proximity
Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identificationSemantic proximity
Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application studySemantic proximity
Forecasting Outcomes of Assisted Reproductive Treatments Using Artificial Networks (FORTUNE) classification system: a new prognostic model to predict euploid blastocyst yield in patients undergoing IVFSemantic proximity
Advanced KPI Framework for IVF Pregnancy Prediction Models in IVF protocolsSemantic proximity
Exploring the efficacy and beneficial population of preimplantation genetic testing for aneuploidy start from the oocyte retrieval cycle: a real-world studySemantic proximity
Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cyclesSemantic proximity
Genetic Results and Clinical Pregnancy Outcomes Following Preimplantation Genetic Testing: A Retrospective Analysis of 2577 Embryo BiopsiesSemantic proximity
Select a neighboring publication to make it the new centre.