Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging

Suraj Rajendran, Matthew Brendel, Josue Barnes, Qiansheng Zhan, Jonas E. Malmsten, Pantelis Zisimopoulos, Alexandros Sigaras, Kwabena Ofori-Atta, Marcos Meseguer, Kathleen A. Miller, David Hoffman, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic and Iman Hajirasouliha ()
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Suraj Rajendran: Weill Cornell Medicine of Cornell University
Matthew Brendel: Weill Cornell Medicine of Cornell University
Josue Barnes: Weill Cornell Medicine of Cornell University
Qiansheng Zhan: Weill Cornell Medicine
Jonas E. Malmsten: Weill Cornell Medicine
Pantelis Zisimopoulos: Weill Cornell Medicine of Cornell University
Alexandros Sigaras: Weill Cornell Medicine of Cornell University
Kwabena Ofori-Atta: Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program
Marcos Meseguer: Health Research Institute la Fe
Kathleen A. Miller: IVF Florida Reproductive Associates
David Hoffman: IVF Florida Reproductive Associates
Zev Rosenwaks: Weill Cornell Medicine
Olivier Elemento: Weill Cornell Medicine of Cornell University
Nikica Zaninovic: Weill Cornell Medicine
Iman Hajirasouliha: Weill Cornell Medicine of Cornell University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists’ manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.

Date: 2024
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DOI: 10.1038/s41467-024-51823-7

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