Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos
Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos
January 5th, 2022
Authors:
Kevin E. Loewke, Ph.D.
Justina Hyunjii Cho, M.S., M.A.
Camelia D. Brumar, B.S.
Paxton Maeder-York, M.S., M.B.A.
Oleksii O. Barash, Ph.D.
Jonas E. Malmsten, D.P.S.
Nikica Zaninovic, Ph.D.
Denny Sakkas, Ph.D.
Kathleen A. Miller, D.H.Sc.
Michael Levy, M.D.
Matthew David VerMilyea, H.C.L.D, Ph.D.
DOI: https://doi.org/10.1016/j.fertnstert.2021.11.022
Abstract
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 centers in the United States.
Patient(s):
Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts.