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Should there be an “AI” in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm

  • Assisted Reproduction Technologies
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Purpose

A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm.

Materials and methods

A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI.

Results

Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior).

Conclusions

The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.

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Funding

This work was partially supported by the Brigham Precision Medicine Developmental Award (Brigham Precision Medicine Program, Brigham and Women’s Hospital), Partners Innovation Discovery Grant (Partners Healthcare), and R01AI118502, and R01AI138800.

Author information

Authors and Affiliations

  1. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    V. W. Fitz, I. Dimitriadis, I. Souter & C. L. Bormann

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    M. K. Kanakasabapathy, P. Thirumalaraju & H. Kandula

  3. Northwell Health Fertility, Manhasset, NY, USA

    L. B. Ramirez

  4. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Wisconsin, Madison, WI, USA

    L. Boehnlein

  5. CCRM Fertility Network, Lone Tree, CO, USA

    J. E. Swain

  6. CCRM Fertility Orange County, Newport Beach, CA, USA

    C. L. Curchoe

  7. The Deborah Kelly Center for Outcomes Research, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA

    K. James

  8. Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    H. Shafiee

Authors
  1. V. W. Fitz
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  2. M. K. Kanakasabapathy
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  3. P. Thirumalaraju
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  4. H. Kandula
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  5. L. B. Ramirez
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  6. L. Boehnlein
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  7. J. E. Swain
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  8. C. L. Curchoe
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  9. K. James
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  10. I. Dimitriadis
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  11. I. Souter
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  12. C. L. Bormann
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  13. H. Shafiee
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by CB, KJ, and VWF. The fist draft of the manuscript was written by VWF, and all authors commented on previous version of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to C. L. Bormann or H. Shafiee.

Ethics declarations

Ethics approval

Approval for this study was obtained from the ethics committee of Massachusetts General Hospital (IRB #2017P001339 and #2019P002392).

Conflict of interest

CB, HS, MK, and PT all have a patent WO2019068073A1 pending in relation to this work. Other authors declare no competing interests.

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Fitz, V.W., Kanakasabapathy, M.K., Thirumalaraju, P. et al. Should there be an “AI” in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet 38, 2663–2670 (2021). https://doi.org/10.1007/s10815-021-02318-7

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  • DOI: https://doi.org/10.1007/s10815-021-02318-7

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