Consistency and objectivity of automated embryo assessments using deep neural networks

Evaluation of a deep-learning based approach for the assessment of embryos, with respect to its consistency and objectivity, in the tasks of embryo scoring and disposition decisions for cryopreservation and biopsy, compared to grading by trained embryologists.

Volume 113, Issue 4, Pages 781–787.e1


Charles L. Bormann, Ph.D., Prudhvi Thirumalaraju, B. Tech, Manoj Kumar Kanakasabapathy, M. Tech, Hemanth Kandula, B. Tech, Irene Souter, M.D., Irene Dimitriadis, M.D., Ph.D., Raghav Gupta, B. Tech, Rohan Pooniwala, B. Tech, Hadi Shafiee, Ph.D.



To evaluate the consistency and objectivity of deep neural networks in embryo scoring and making disposition decisions for biopsy and cryopreservation in comparison to grading by highly trained embryologists.


Prospective double-blind study using retrospective data.


U.S.-based large academic fertility center.


Not applicable.


Embryo images (748 recorded at 70 hours postinsemination [hpi]) and 742 at 113 hpi) were used to evaluate embryologists and neural networks in embryo grading. The performance of 10 embryologists and a neural network were also evaluated in disposition decision making using 56 embryos.

Main Outcome Measures

Coefficients of variation (%CV) and measures of consistencies were compared.


Embryologists exhibited a high degree of variability (%CV averages: 82.84% for 70 hpi and 44.98% for 113 hpi) in grading embryo. When selecting blastocysts for biopsy or cryopreservation, embryologists had an average consistency of 52.14% and 57.68%, respectively. The neural network outperformed the embryologists in selecting blastocysts for biopsy and cryopreservation with a consistency of 83.92%. Cronbach’s α analysis revealed an α coefficient of 0.60 for the embryologists and 1.00 for the network.


The results of our study show a high degree of interembryologist and intraembryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading. This may ultimately lead to less precise disposition decisions and discarding of viable embryos. The application of a deep neural network, as shown in our study, can introduce improved reliability and high consistency during the process of embryo selection and disposition, potentially improving outcomes in an embryology laboratory.

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