Journal
LAB ON A CHIP
Volume 19, Issue 24, Pages 4139-4145Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9lc00721k
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Funding
- Massachusetts General Hospital
- Brigham and Women's Hospital (BWH) Center for Clinical Data Science
- BWH Precision Medicine Developmental Award (BWH Precision Medicine Program)
- Partners Innovation Discovery Grant (Partners Healthcare)
- National Institutes of Health [1R01AI118502, R01AI138800]
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Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.
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