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

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



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.


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.


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).


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.

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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).

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Authors and Affiliations



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.

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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.

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

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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).

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