{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:11:46Z","timestamp":1790244706126,"version":"4.1.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032382597","type":"print"},{"value":"9783032382603","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-38260-3_5","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:56:23Z","timestamp":1790243783000},"page":"47-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Born Different: A Multi-level Individualized Modeling Framework for\u00a0Embryo Euploidy Prediction"],"prefix":"10.1007","author":[{"given":"Shuangyi","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxian","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyun","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sibei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanbin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"1375474","DOI":"10.3389\/frai.2024.1375474","volume":"7","author":"MR Borna","year":"2024","unstructured":"Borna, M.R., Sepehri, M.M., Maleki, B.: An artificial intelligence algorithm to select most viable embryos considering current process in ivf labs. Front. Artif. Intell. 7, 1375474 (2024)","journal-title":"Front. Artif. Intell."},{"issue":"1","key":"5_CR2","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10815-021-02336-5","volume":"39","author":"C Bruno","year":"2022","unstructured":"Bruno, C., et al.: Analysis and quantification of female and male contributions to the first stages of embryonic kinetics: study from a time-lapse system. J. Assist. Reprod. Genet. 39(1), 85\u201395 (2022)","journal-title":"J. Assist. Reprod. Genet."},{"issue":"10","key":"5_CR3","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1007\/s10815-025-03658-4","volume":"42","author":"R Cannarella","year":"2025","unstructured":"Cannarella, R., et al.: Impact of male factors on morphokinetic parameters: a prospective analysis using time-lapse monitored embryos. J. Assist. Reprod. Genet. 42(10), 3551\u20133560 (2025)","journal-title":"J. Assist. Reprod. Genet."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Automating blastocyst formation and quality prediction in time-lapse imaging with adaptive key frame selection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 445\u2013455. Springer (2022)","DOI":"10.1007\/978-3-031-16440-8_43"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Elkhatib, I., et al.: The influence of male age and sperm parameters on blastulation and euploidy rates. Fertil. Steril. (2025)","DOI":"10.1016\/j.fertnstert.2025.06.029"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Eswaran, M., Balasubramanie, P., Salman, J.M.: Deep learning algorithms for timelapse image sequence-based automated blastocyst quality detection. In: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 1246\u20131252. IEEE (2023)","DOI":"10.1109\/ICESC57686.2023.10193708"},{"issue":"3","key":"5_CR7","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1093\/humrep\/dead001","volume":"38","author":"K Ezoe","year":"2023","unstructured":"Ezoe, K., et al.: Maternal age affects pronuclear and chromatin dynamics, morula compaction and cell polarity, and blastulation of human embryos. Hum. Reprod. 38(3), 387\u2013399 (2023)","journal-title":"Hum. Reprod."},{"issue":"2","key":"5_CR8","first-page":"110","volume":"25","author":"N Handayani","year":"2024","unstructured":"Handayani, N., Danardono, G.B., et al.: Improving deep learning-based algorithm for ploidy status prediction through combined u-net blastocyst segmentation and sequential time-lapse blastocysts images. J. Reprod. Infertility 25(2), 110 (2024)","journal-title":"J. Reprod. Infertility"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"He, C., et al.: Embryo graphs: Predicting human embryo viability from 3d morphology. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 80\u201390. Springer (2024)","DOI":"10.1007\/978-3-031-72083-3_8"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"He, H., Wu, L.: A novel non-invasive embryo evaluation method (nics-timelapse) with enhanced predictive precision and clinical impact. Heliyon 10(9), (2024)","DOI":"10.1016\/j.heliyon.2024.e30189"},{"issue":"1","key":"5_CR11","doi-asserted-by":"publisher","first-page":"28019","DOI":"10.1038\/s41598-024-79175-8","volume":"14","author":"K Kalyani","year":"2024","unstructured":"Kalyani, K., Deshpande, P.S.: A deep learning model for predicting blastocyst formation from cleavage-stage human embryos using time-lapse images. Sci. Rep. 14(1), 28019 (2024)","journal-title":"Sci. Rep."},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Kim, J., et\u00a0al.: Multimodal learning for embryo viability prediction in clinical ivf. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 542\u2013552. Springer (2024)","DOI":"10.1007\/978-3-031-72086-4_51"},{"key":"5_CR13","unstructured":"Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"103494","DOI":"10.1016\/j.compbiomed.2019.103494","volume":"115","author":"MF Kragh","year":"2019","unstructured":"Kragh, M.F., Rimestad, J., Berntsen, J., Karstoft, H.: Automatic grading of human blastocysts from time-lapse imaging. Comput. Biol. Med. 115, 103494 (2019)","journal-title":"Comput. Biol. Med."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Lockhart, L., Saeedi, P., Au, J., Havelock, J.: Automating embryo development stage detection in time-lapse imaging with synergic loss and temporal learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 540\u2013549. Springer (2021)","DOI":"10.1007\/978-3-030-87240-3_52"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Majee, A., Xenochristou, M., Chen, W.P.: Tabglm: tabular graph language model for learning transferable representations through multi-modal consistency minimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 19387\u201319395 (2025)","DOI":"10.1609\/aaai.v39i18.34134"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Ou, Z., et al.: Classification of human embryos by using deep learning. In: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), vol. 12712, pp. 342\u2013347. SPIE (2023)","DOI":"10.1117\/12.2678962"},{"key":"5_CR18","unstructured":"Ouyang, X., Wei, J., Huo, W., Wang, X., Li, R., Zhou, J.: Defusion: an effective decoupling fusion network for multi-modal pregnancy prediction. arXiv preprint arXiv:2501.04353 (2025)"},{"issue":"1","key":"5_CR19","doi-asserted-by":"publisher","first-page":"7756","DOI":"10.1038\/s41467-024-51823-7","volume":"15","author":"S Rajendran","year":"2024","unstructured":"Rajendran, S., et al.: Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging. Nat. Commun. 15(1), 7756 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"5_CR20","doi-asserted-by":"publisher","first-page":"6235","DOI":"10.1038\/s41467-025-61116-2","volume":"16","author":"S Rajendran","year":"2025","unstructured":"Rajendran, S., et al.: A foundational model for in vitro fertilization trained on 18 million time-lapse images. Nat. Commun. 16(1), 6235 (2025)","journal-title":"Nat. Commun."},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Sun, Y., et al.: Time-lapse video-based embryo grading via complementary spatial-temporal pattern mining. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 594\u2013604. Springer (2025)","DOI":"10.1007\/978-3-032-05169-1_57"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Tan, S., et al.: Atri-net: a multimodal and multifocal fusion network for embryo euploidy prediction. In: 2026 IEEE 23th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2026)","DOI":"10.1109\/ISBI61048.2026.11515415"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Truong, V.V., et al.: A deep learning approach to embryo quality assessment. In: The International Conference on Intelligent Systems & Networks, pp. 452\u2013461. Springer (2024)","DOI":"10.1007\/978-981-97-5504-2_54"},{"key":"5_CR24","doi-asserted-by":"publisher","first-page":"18927","DOI":"10.1109\/ACCESS.2021.3053098","volume":"9","author":"S Wang","year":"2021","unstructured":"Wang, S., Zhou, C., Zhang, D., Chen, L., Sun, H.: A deep learning framework design for automatic blastocyst evaluation with multifocal images. IEEE Access 9, 18927\u201318934 (2021)","journal-title":"IEEE Access"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Xie, X., Yan, P., Cheng, F.Y., Gao, F., Mai, Q., Li, G.: Early prediction of blastocyst development via time-lapse video analysis. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761654"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Ye, D., Ye, Z., Zhang, H.: Effects of sperm dna fragmentation on embryo morphokinetic parameters and laboratory outcomes in women of different ages during intracytoplasmic sperm treatment cycles. Arch. Gynecol. Obstet. 1\u20136 (2025)","DOI":"10.1007\/s00404-025-07949-0"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38260-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:56:30Z","timestamp":1790243790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38260-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032382597","9783032382603"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38260-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}