Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos

5 Jan 2022

Abstract excerpt

OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN: Retrospective study. SETTING: Consortium of 11 assisted reproductive technology...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Fetal and Pediatric Neurological Disorders
Share to Prenatal Screening and Diagnostics
Share to Reproductive Biology and Fertility

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 34998577
DOI: 10.1016/j.fertnstert.2021.11.022

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryosPMID 34998577Read ↗

An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryosSemantic proximity Read ↗

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy lossSemantic proximity Read ↗

Embryo selection with artificial intelligence: how to evaluate and compare methods?Semantic proximity Read ↗

Intelligent assisted reproduction: Innovative applications of artificial intelligence in embryo health assessmentSemantic proximity Read ↗

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryosSemantic proximity Read ↗

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequencesSemantic proximity Read ↗

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilizationSemantic proximity Read ↗

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation studySemantic proximity Read ↗

Artificial intelligence outperforms humans in morphology-based oocyte selection in cattleSemantic proximity Read ↗

Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVFSemantic proximity Read ↗

Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by ageSemantic proximity Read ↗

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A CyclesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.