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.

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Volume 113, Issue 4, Pages 781–787.e1

Authors:

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.

Abstract:

Objective

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.

Design

Prospective double-blind study using retrospective data.

Setting

U.S.-based large academic fertility center.

Patients

Not applicable.

Intervention(s)

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.

Results

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.

Conclusions

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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Fertility and Sterility

Editorial Office, American Society for Reproductive Medicine

Fertility and Sterility® is an international journal for obstetricians, gynecologists, reproductive endocrinologists, urologists, basic scientists and others who treat and investigate problems of infertility and human reproductive disorders. The journal publishes juried original scientific articles in clinical and laboratory research relevant to reproductive endocrinology, urology, andrology, physiology, immunology, genetics, contraception, and menopause. Fertility and Sterility® encourages and supports meaningful basic and clinical research, and facilitates and promotes excellence in professional education, in the field of reproductive medicine.

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