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Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI)



Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).


Pregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test.


Actual implantation rates following ET performed by one MD provider: “H” was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, “H” (30% vs. 60%, p = 0.011). Embryos thawed by embryologist “H” had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW.


AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

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This work was partially supported by the Brigham Precision Medicine Developmental Award and Innovation Evergreen Fund (Brigham Precision Medicine Program, Brigham and Women’s Hospital), Partners Innovation Discovery Grant (Partners Healthcare): IGNITE Award (Connors Center for Women's Health & Gender Biology, Brigham and Women's Hospital) and R01AI138800, R01EB033866, R33AI140489, and R61AI140489 (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 Panagiotis Cherouveim, Charles Bormann, Victoria S. Jiang, Manoj Kumar Kanakasabapathy, and Prudhvi Thirumalaraju. The first draft of the manuscript was written by Panagiotis Cherouveim, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hadi Shafiee.

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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#2019P001000) 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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Cherouveim, P., Jiang, V.S., Kanakasabapathy, M.K. et al. Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI). J Assist Reprod Genet 40, 241–249 (2023).

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