Performance of a deep learning based neural network in the selection of human blastocysts for implantation
- Massachusetts General Hospital, Harvard Medical School, United States
- Brigham and Women's Hospital, Harvard Medical School, United States
- Extend Fertility, United States
- San Diego Fertility Center, United States
- Colorado Center for Reproductive Medicine IVF Laboratory Network, United States
- University of Wisconsin, United States
Abstract
Deep learning in in-vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistive tools and algorithms that can work with static images, however, can help in improving the access to care by enabling their use with images acquired from traditional microscopes that are available to virtually all fertility centers. Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single timepoint images of embryos collected at 113 hours post-insemination, in embryo selection amongst 97 clinical patient cohorts (742 embryos) and observed an accuracy of 90% in choosing the highest quality embryo available. Furthermore, a CNN trained to assess an embryo’s implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, P<0.0001) from 5 different fertility centers.
Data availability
Patients did not explicitly consent to their data being made public and access is therefore restricted. Requests for the anonymized data should be made to Charles Bormann (cbormann@partners.org) and Hadi Shafiee (hshafiee@bwh.harvard.edu). Requests will be reviewed by a data access committee, taking into account the research proposal and intended use of the data. Requestors are required to sign a data-sharing agreement to ensure patients' confidentiality is maintained prior to the release of any data.
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Author details
Funding
National Institutes of Health (R01AI138800)
- Hadi Shafiee
Brigham and Women's Hospital (Precision Medicine Program)
- Hadi Shafiee
Partners Healthcare (Innovation Discovery Grant)
- Charles L Bormann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Embryo image/video data collected from patients were used in this study with an institutional review board approval (IRB#2017P001339) We used 3,469 recorded videos of embryos collected from patients with informed consent for research and publication, under an institutional review board approval for secondary research use.
Copyright
© 2020, Bormann et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Performance of a deep learning based neural network in the selection of human blastocysts for implantation
eLife 9:e55301.
https://doi.org/10.7554/eLife.55301
