Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology

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. Availabilit...

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Bibliographic Details
Published in:Lab on a chip 2019-12, Vol.19 (24), p.4139-4145
Main Authors: Kanakasabapathy, Manoj Kumar, Thirumalaraju, Prudhvi, Bormann, Charles L, Kandula, Hemanth, Dimitriadis, Irene, Souter, Irene, Yogesh, Vinish, Kota Sai Pavan, Sandeep, Yarravarapu, Divyank, Gupta, Raghav, Pooniwala, Rohan, Shafiee, Hadi
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Blastocyst
Decision analysis
Deep Learning
Embryology
Embryonic Development
Embryos
Evaluation
Fertilization in Vitro
Humans
Image acquisition
Image classification
Image Processing, Computer-Assisted
Image quality
Image resolution
In vitro fertilization
Morphology
System effectiveness
Test sets
Time-Lapse Imaging
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Summary: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 (
ISSN:1473-0197
1473-0189
1473-0189
DOI:10.1039/c9lc00721k