Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Analysis of morphological, morphokinetics, cell-free DNA, microRNAs parameters to predict aneuploidy status of embryos.

PloS one·2026
Same author

Luteinizing Hormone β-subunit Gene Polymorphisms and Androgen Levels are Less Predictive of Ovarian Response in Polycystic Ovary Syndrome <i>In vitro</i> Fertilisation Women: A Nested Case-Control Study.

Journal of human reproductive sciences·2026
Same author

Advanced Embryo Ploidy Classification Using Vision Transformers: Integration of Sequential Time-Lapse Imaging and Undersampling Techniques: A Retrospective Study.

Journal of human reproductive sciences·2026
Same author

Neonatal Bilirubin Assessment After Implementation of Enhanced Recovery After Caesarean Section.

The Journal of perinatal & neonatal nursing·2025
Same author

Impact of Time-Lapse Incubator Systems on Fertilization, Blastocyst Development, and Clinical Pregnancy Outcomes.

Journal of reproduction & infertility·2025
Same author

Enhancing Mesh-Tissue Integration in Menopausal Models Using a Platelet-Rich Plasma-Decellularized Amnion Scaffold Sandwich: A Study on Mesh Contraction, Inflammatory Infiltrate, IL-17, CD31, and Collagen Deposition.

International urogynecology journal·2025

Related Experiment Video

Updated: Jul 17, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
00:09

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

9.5K

Embryo ploidy status classification through computer-assisted morphology assessment.

Gunawan Bondan Danardono1, Nining Handayani1, Claudio Michael Louis1

  • 1IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini).

AJOG Global Reports
|August 30, 2023
PubMed
Summary

This study developed an AI model to predict embryo ploidy status non-invasively, reducing the need for invasive preimplantation genetic testing for aneuploidy. The model achieved 74% accuracy, offering a promising alternative for IVF decision-making.

Keywords:
artificial intelligenceimage processingin vitro fertilizationnoninvasive embryo assessmentploidy statusprediction modelpreimplantation genetic testing for aneuploid

More Related Videos

Human Egg Maturity Assessment and Its Clinical Application
08:51

Human Egg Maturity Assessment and Its Clinical Application

Published on: August 19, 2019

19.3K
Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues
11:54

Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues

Published on: October 20, 2019

9.2K

Related Experiment Videos

Last Updated: Jul 17, 2025

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
00:09

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

9.5K
Human Egg Maturity Assessment and Its Clinical Application
08:51

Human Egg Maturity Assessment and Its Clinical Application

Published on: August 19, 2019

19.3K
Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues
11:54

Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues

Published on: October 20, 2019

9.2K

Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Embryology

Background:

  • Preimplantation genetic testing for aneuploidy (PGT-A) assesses embryo chromosomal status but involves invasive biopsy.
  • Current PGT-A methods have drawbacks, including invasiveness and limited long-term effect data.

Purpose of the Study:

  • To develop a computer-assisted predictive model using machine learning to minimize invasive PGT-A.
  • To predict embryo ploidy status based on morphologic characteristics for improved IVF decision-making.

Main Methods:

  • Utilized image processing techniques, including image augmentation and feature extraction.
  • Developed and compared multiple machine learning and deep learning algorithms for prediction modeling.
  • Employed histogram of oriented gradient and principal component analysis for image analysis.

Main Results:

  • An artificial intelligence model was successfully developed to predict embryo ploidy status.
  • The gradient boosting algorithm demonstrated superior performance with 0.74 accuracy.
  • Achieved 0.83 aneuploid precision and 0.84 aneuploid predictive value (recall).

Conclusions:

  • Machine-assisted technology offers a novel perspective on embryo assessment compared to human observation.
  • The developed model serves as a foundation for future, more advanced computer-assisted prediction systems in IVF.
  • Further research is recommended to refine AI models and explore their long-term implications in in vitro fertilization.