V W Fitz1, M K Kanakasabapathy2, P Thirumalaraju2, H Kandula2, L B Ramirez3, L Boehnlein4, J E Swain5, C L Curchoe6, K James7, I Dimitriadis1, I Souter1, C L Bormann8, H Shafiee9. 1. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 2. Department of Medicine, Harvard Medical School, Boston, MA, USA. 3. Northwell Health Fertility, Manhasset, NY, USA. 4. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Wisconsin, Madison, WI, USA. 5. CCRM Fertility Network, Lone Tree, CO, USA. 6. CCRM Fertility Orange County, Newport Beach, CA, USA. 7. The Deborah Kelly Center for Outcomes Research, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA. 8. Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. cbormann@partners.org. 9. Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
Abstract
PURPOSE: A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS: A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS: Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior). CONCLUSIONS: The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
PURPOSE: A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS: A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS: Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior). CONCLUSIONS: The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
Authors: Santiago Munné; Brian Kaplan; John L Frattarelli; Tim Child; Gary Nakhuda; F Nicholas Shamma; Kaylen Silverberg; Tasha Kalista; Alan H Handyside; Mandy Katz-Jaffe; Dagan Wells; Tony Gordon; Sharyn Stock-Myer; Susan Willman Journal: Fertil Steril Date: 2019-09-21 Impact factor: 7.329
Authors: Matthew A Shear; Denis A Vaughan; Anna M Modest; Emily A Seidler; Angela Q Leung; Michele R Hacker; Denny Sakkas; Alan S Penzias Journal: Reprod Biomed Online Date: 2020-07-22 Impact factor: 3.828