Skip to main content

Advertisement

Log in

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study

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

Abstract  

Purpose

To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.

Methods

A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient’s unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured.

Results

CNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n = 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n = 400 patients; 3 replicates).

Conclusions

This study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. To err is human. January 10, 2021]; Available from: https://www.merriam-webster.com/dictionary/to%20err%20is%20human.

  2. de los Santos, M.J. and A. Ruiz, Protocols for tracking and witnessing samples and patients in assisted reproductive technology. Fertil Steril, 2013. 100(6): p. 1499–502.

  3. Letterie G. Outcomes of medical malpractice claims in assisted reproductive technology over a 10-year period from a single carrier. J Assist Reprod Genet. 2017;34(4):459–63.

    Article  Google Scholar 

  4. Rasouli MA, Moutos CP, Phelps JY. Liability for embryo mix-ups in fertility practices in the USA. J Assist Reprod Genet. 2021;38(5):1101–7.

    Article  Google Scholar 

  5. Sakkas D, Pool TB, Barrett CB. Analyzing IVF laboratory error rates: highlight or hide? Reprod Biomed Online. 2015;31(4):447–8.

    Article  CAS  Google Scholar 

  6. Adverse incidents in fertility clinics: lessons to learn. 2014 January 28, 2021]; Available from: hfea.gov.uk/media/1146/incidents_report_2014_designed_-_web_final.pdf.

  7. Cimadomo D, et al. Failure mode and effects analysis of witnessing protocols for ensuring traceability during PGD/PGS cycles. Reprod Biomed Online. 2016;33(3):360–9.

    Article  Google Scholar 

  8. Novo S, et al. Direct embryo tagging and identification system by attachment of biofunctionalized polysilicon barcodes to the zona pellucida of mouse embryos. Hum Reprod. 2013;28(6):1519–27.

    Article  Google Scholar 

  9. Fitz VW, 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. 2021;38(10):2663–70.

    Article  CAS  Google Scholar 

  10. Bormann, C.L., et al., Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril, 2020. 113(4) 781–787 e1.

  11. Manoj Kumar Kanakasabapathy, P.T., Charles L Bormann, Raghav Gupta, Rohan Pooniwala, Hemanth Kandula, Irene Souter, Irene Dimitriadis, Hadi Shafiee, Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage. 2006.

  12. Bormann, C.L., et al., Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife, 2020. 9.

  13. Thirumalaraju P, et al. Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality. Heliyon. 2021;7(2): e06298.

    Article  Google Scholar 

  14. Bormann CL, et al. Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory. J Assist Reprod Genet. 2021;38(7):1641–6.

    Article  Google Scholar 

  15. A Meyer, J.D., N Kelly, H Kandula, M Kanakasabapathy, P Thirumalaraju, C Bormann, H Shafiee, Can deep convolutional neural network (CNN) be used as a non-invasive method to replace Preimplantation Genetic Testing for Aneuploidy (PGT-A)? . Human Reproduction, 2020. 35 1238.

  16. M Kanakasabapathy, C.B., P Thirumalaraju, R Banerjee, H Shafiee, Improving the performance of deep convolutional neural networks (CNN) in embryology using synthetic machine-generated images. Human Reproduction, 2020. 35 1209.

  17. Dimitriadis, C.L.B., M.K. Kanakasabapathy, P. Thirumalaraju, R. Gupta, R. Pooniwala, I. Souter, S.T. Rice, P. Bhowmick, H. Shafiee, Deep convolutional neural networks (CNN) for assessment and selection of normally fertilized human embryos. Fertility and Sterility. 112 272.

  18. Prudhvi Thirumalaraju, M.K.K., Charles L. Bormann, Hemanth Kandula, Sandeep Kota Sai Pavan, Divyank Yarravarapu, Hadi Shafiee, Human sperm morphology analysis using smartphone microscopy and deep learning. Fertility and Sterility, 2019. 112(3) 41.

  19. Holmes R, et al. Comparison of electronic versus manual witnessing of procedures within the in vitro fertilization laboratory: impact on timing and efficiency. F S Rep. 2021;2(2):181–8.

    PubMed  PubMed Central  Google Scholar 

  20. Rienzi L, et al. Failure mode and effects analysis of witnessing protocols for ensuring traceability during IVF. Reprod Biomed Online. 2015;31(4):516–22.

    Article  Google Scholar 

  21. Forte M, et al. Electronic witness system in IVF-patients perspective. J Assist Reprod Genet. 2016;33(9):1215–22.

    Article  Google Scholar 

  22. Hur YS, et al. Development of a security system for assisted reproductive technology (ART). J Assist Reprod Genet. 2015;32(1):155–68.

    Article  Google Scholar 

  23. Perrin RA, Simpson N. RFID and bar codes–critical importance in enhancing safe patient care. J Healthc Inf Manag. 2004;18(4):33–9.

    PubMed  Google Scholar 

  24. Sato T, et al. Radiofrequency identification tag system improves the efficiency of closed vitrification for cryopreservation and thawing of bovine ovarian tissues. J Assist Reprod Genet. 2019;36(11):2251–7.

    Article  CAS  Google Scholar 

  25. Fiocchi S, et al. Temperature increase in the fetus exposed to UHF RFID readers. IEEE Trans Biomed Eng. 2014;61(7):2011–9.

    Article  Google Scholar 

  26. Aitken RJ, et al. Impact of radio frequency electromagnetic radiation on DNA integrity in the male germline. Int J Androl. 2005;28(3):171–9.

    Article  CAS  Google Scholar 

  27. Rienzi L, et al. Comprehensive protocol of traceability during IVF: the result of a multicentre failure mode and effect analysis. Hum Reprod. 2017;32(8):1612–20.

    Article  CAS  Google Scholar 

Download references

Funding

This work was partially supported by the Brigham Precision Medicine Developmental Award (Brigham Precision Medicine Program, Brigham and Women’s Hospital), Innovation Evergreen Fund (Brigham and Women’s Hospital), Partners Innovation Discovery Grant (Partners Healthcare), and R01AI118502, R01AI138800, R61AI40489, and 4U54HL119145-08 (National Institute of Health).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Charles L. Bormann, Manoj Kumar Kanakasabapathy, and Prudhvi Thirumalaraju. The first draft of the manuscript was written by Karissa C. Hammer and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Karissa C. Hammer, Victoria S. Jiang, Charles L. Bormann or Hadi Shafiee.

Ethics declarations

Ethics approval and consent to participate

Informed consent was obtained from each individual before participation. Study protocols were approved by the Institutional Review Board (IRB#2017P001339) at Massachusetts General Hospital and Brigham and Women’s Hospital.

Consent for publication

Not applicable.

Conflict of interest

Authors Dr. Hadi Shafiee, Dr. Charles Bormann, Prudhvi Thirumalaraju, and Manoj Kumar Kanakasabapathy wish to disclose a patent, currently licensed by a commercial entity, on the use of AI for embryology (US11321831B2). The rest of the authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Karissa C. Hammer and Victoria S. Jiang are co-first authors.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hammer, K.C., Jiang, V.S., Kanakasabapathy, M.K. et al. Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study. J Assist Reprod Genet 39, 2343–2348 (2022). https://doi.org/10.1007/s10815-022-02585-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10815-022-02585-y

Keywords

Navigation