{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T00:29:09Z","timestamp":1792283349495,"version":"4.4.0"},"reference-count":38,"publisher":"eLife Sciences Publications, Ltd","license":[{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T00:00:00Z","timestamp":1600128000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI138800"],"award-info":[{"award-number":["R01AI138800"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005292","name":"Brigham and Women's Hospital","doi-asserted-by":"publisher","award":["Precision Medicine Program"],"award-info":[{"award-number":["Precision Medicine Program"]}],"id":[{"id":"10.13039\/100005292","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007806","name":"Partners Healthcare","doi-asserted-by":"publisher","award":["Innovation Discovery Grant"],"award-info":[{"award-number":["Innovation Discovery Grant"]}],"id":[{"id":"10.13039\/100007806","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R61AI140489"],"award-info":[{"award-number":["R61AI140489"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elifesciences.org"],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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 hr 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\u2019s implantation potential directly using a set of 97 euploid embryos capable of implantation outperformed 15 trained embryologists (75.26% vs. 67.35%, p&lt;0.0001) from five different fertility centers.<\/jats:p>","DOI":"10.7554\/elife.55301","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T20:00:32Z","timestamp":1600113632000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.55301","source":"Crossref","is-referenced-by-count":153,"title":["Performance of a deep learning based neural network in the selection of human blastocysts for implantation"],"prefix":"10.7554","volume":"9","author":[{"given":"Charles L","family":"Bormann","sequence":"first","affiliation":[{"name":"Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, United States"},{"name":"Harvard Medical School, Boston, United 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