{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T23:00:35Z","timestamp":1789340435645,"version":"build-2803163510"},"reference-count":0,"publisher":"Knowledge E DMCC","content-domain":{"domain":["kne-publishing.com","knepublishing.com","jri.ir"],"crossmark-restriction":true},"short-container-title":["JRI"],"abstract":"<jats:p>Background: The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI).\r\nMethods: Time-lapse videos of embryo development were manually annotated by the embryologist and extracted for use as a supervised dataset, where the data were split into 14 unique classifications based on morphological differences. A compilation of homogeneous pre-trained CNN models obtained via TensorFlow Hub was tested with various hyperparameters on a controlled environment using transfer learning to create a new model. Subsequently, the performances of the AI models in correctly annotating embryo morphologies within the 14 designated classifications were compared with a collection of AI models with different built-in configurations so as to derive a model with the highest accuracy.\r\nResults: Eventually, an AI model with a specific configuration and an accuracy score of 67.68% was obtained, capable of predicting the embryo developmental stages (t1, t2, t3, t4, t5, t6, t7, t8, t9+, tCompaction, tM, tSB, tB, tEB).\r\nConclusion: Currently, the technology and research of artificial intelligence and machine learning in the medical field have significantly and continuingly progressed in an effort to develop computer-assisted technology which could potentially increase the efficiency and accuracy of medical personnel\u2019s performance. Nonetheless, building AI models with larger data is required to properly increase AI model reliability.\u00a0\u00a0\u00a0<\/jats:p>","DOI":"10.18502\/jri.v23i4.10809","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T05:17:29Z","timestamp":1665465449000},"update-policy":"https:\/\/doi.org\/10.18502\/knecrossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics"],"prefix":"10.18502","author":[{"given":"Gunawan B.","family":"Danardono","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alva","family":"Erwin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Purnama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nining","family":"Handayani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arie A.","family":"Polim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arief","family":"Boediono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Sini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7770","published-online":{"date-parts":[[2022,10,11]]},"container-title":["Journal of Reproduction &amp; Infertility"],"original-title":[],"link":[{"URL":"https:\/\/publish.kne-publishing.com\/index.php\/JRI\/article\/download\/10809\/10208","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publish.kne-publishing.com\/index.php\/JRI\/article\/download\/10809\/10208","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:41:24Z","timestamp":1772210484000},"score":1,"resource":{"primary":{"URL":"https:\/\/publish.kne-publishing.com\/index.php\/JRI\/article\/view\/10809"},"secondary":[{"URL":"https:\/\/www.jri.ir\/article\/140161","label":"UNIVERSITY"},{"URL":"https:\/\/publish.kne-publishing.com\/index.php\/JRI\/article\/view\/10809","label":"PUBLISHER"}]},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.18502\/jri.v23i4.10809","relation":{},"ISSN":["2251-676X","2228-5482"],"issn-type":[{"value":"2251-676X","type":"electronic"},{"value":"2228-5482","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10,11]]}}}