Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer
TLDR
A retrospective analysis demonstrating that the deep learning model has a high level of predictability of the likelihood that an embryo will implant and may improve the effectiveness of previous approaches used for time-lapse imaging in embryo selection.Abstract:
STUDY QUESTION
Can a deep learning model predict the probability of pregnancy with fetal heart (FH) from time-lapse videos?read more
Citations
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Journal ArticleDOI
Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.
M VerMilyea,Jonathan M. M. Hall,S Diakiw,Adrian Johnston,Tan H. Nguyen,D Perugini,A Miller,A Picou,Annalee Murphy,Michelle Perugini +9 more
TL;DR: The Life Whisperer AI model demonstrated an improved predictive ability for evaluation of embryo viability when compared with embryologists’ traditional morphokinetic grading methods, and could lead to improved pregnancy success rates in IVF when used in a clinical setting.
Journal ArticleDOI
Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018.
TL;DR: AI and ML are clearly burgeoning methodologies in human reproduction and embryology and would benefit from early application of reporting standards and large variability on accepted dataset sizes.
Journal ArticleDOI
Good practice recommendations for the use of time-lapse technology †
Susanna Apter,T. Ebner,Thomas Fréour,Yves Guns,Borut Kovacic,Nathalie Le Clef,Monica Marques,Marcos Meseguer,Debbie Montjean,Ioannis A. Sfontouris,Roger G. Sturmey,Giovanni Coticchio +11 more
TL;DR: The present ESHRE document provides 11 recommendations on how to introduce TLT in the IVF laboratory, which are mostly based on clinical and technical expertise, but leaves any decision on whether or not to use TLT to the individual centres.
Journal ArticleDOI
Artificial intelligence in human in vitro fertilization and embryology
Nikica Zaninovic,Zev Rosenwaks +1 more
TL;DR: An overview of existing AI technologies in reproductive medicine is presented and the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments.
Journal ArticleDOI
Performance of a deep learning based neural network in the selection of human blastocysts for implantation.
Charles L. Bormann,Manoj Kumar Kanakasabapathy,Prudhvi Thirumalaraju,Raghav Gupta,Rohan Pooniwala,Hemanth Kandula,Eduardo Hariton,Irene Souter,Irene Dimitriadis,Leslie B. Ramirez,Carol Lynn Curchoe,Jason E. Swain,Lynn M. Boehnlein,Hadi Shafiee,Hadi Shafiee +14 more
TL;DR: A CNN trained to assess an embryo’s implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists from five different fertility centers.
References
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Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer
TL;DR: The ability to transfer one high-scoring blastocyst should lead to pregnancy rates greater than 60%, without the complication of twins, according to a retrospective review of blastocysts transfer in an IVF clinic.
Journal ArticleDOI
The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting
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The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting
Basak Balaban,Daniel R. Brison,Gloria Calderón,James Catt,Joe Conaghan,Lisa Cowan,Thomas Ebner,David K. Gardner,Thorir Hardarson,Kersti Lundin,M. Cristina Magli,David Mortimer,Sharon T. Mortimer,Santiago Munné,Dominique Royere,L.A. Scott,Johan Smitz,Alan R. Thornhill,Jonathan Van Blerkom,Etienne Van den Abbeel +19 more