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Kanakasabapathy MK, Thirumalaraju P, Bormann CL, Kandula H, Dimitriadis I, Souter I, Yogesh V, Kota Sai Pavan S, Yarravarapu D, Gupta R, Pooniwala R, Shafiee H. Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology. LAB ON A CHIP 2019; 19:4139-4145. [PMID: 31755505 PMCID: PMC6934406 DOI: 10.1039/c9lc00721k] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
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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Affiliation(s)
- Manoj Kumar Kanakasabapathy
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Prudhvi Thirumalaraju
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Charles L Bormann
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hemanth Kandula
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Irene Dimitriadis
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Irene Souter
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Vinish Yogesh
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Sandeep Kota Sai Pavan
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Divyank Yarravarapu
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Raghav Gupta
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Rohan Pooniwala
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. and Department of Medicine, Harvard Medical School, Boston, MA, USA
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