{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T10:30:29Z","timestamp":1779186629990,"version":"3.51.4"},"reference-count":15,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: Embryo selection in in vitro fertilization (IVF) aims to prioritize embryos with the highest reproductive potential. While preimplantation genetic testing for aneuploidy (PGT-A) remains the gold standard for identifying euploid embryos, it is invasive and not universally applicable. Deep learning (DL)-based models, such as the intelligent data analysis (iDA) score, have emerged as non-invasive alternatives for embryo assessment. This review critically evaluates the relationship between iDAScore (versions 1.0 and 2.0), embryo euploidy, and clinical outcomes, including live birth and miscarriage rates. Methods: A narrative review was performed using PubMed and Google Scholar, covering studies published from January 2020 to May 2025. The search included terms such as \u201ciDAScore,\u201d \u201cdeep learning,\u201d \u201ceuploidy,\u201d and \u201clive birth.\u201d Only English-language full-text studies assessing the predictive performance of iDAScore relative to chromosomal status or reproductive outcomes were included. Results: Six retrospective studies met the inclusion criteria. All reported a statistically significant association between higher iDAScore values and embryo euploidy. AUC values for euploidy prediction ranged from 0.60 to 0.68. In several studies, iDAScore was also positively associated with live birth rates and negatively with miscarriage rates. However, the predictive accuracy was moderate when restricted to euploid embryo cohorts, indicating that iDAScore may be more effective in broader populations where chromosomal status is unknown. Conclusions: iDAScore represents a promising adjunct to traditional embryo assessment. Although it cannot replace PGT-A, it may aid in embryo prioritization when genetic testing is not feasible. Larger prospective studies are warranted to further validate its clinical utility.<\/jats:p>","DOI":"10.3390\/genes16080981","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:40:48Z","timestamp":1755765648000},"page":"981","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Models: Their Relationship with Embryonic Euploidies and Reproductive Outcomes"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-737X","authenticated-orcid":false,"given":"Aikaterini","family":"Selntigia","sequence":"first","affiliation":[{"name":"IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucia","family":"Maresca","sequence":"additional","affiliation":[{"name":"IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diletta","family":"Montanino Oliva","sequence":"additional","affiliation":[{"name":"IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camilla","family":"Coianiz","sequence":"additional","affiliation":[{"name":"IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Galliano","sequence":"additional","affiliation":[{"name":"IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1016\/j.fertnstert.2012.08.016","article-title":"Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: A retrospective cohort study","volume":"98","author":"Meseguer","year":"2012","journal-title":"Fertil. Steril."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Thirumalaraju, P., Kanakasabapathy, M.K., Bormann, C.L., Gupta, R., Pooniwala, R., Kandula, H., Souter, I., Dimitriadis, I., and Shafiee, H. (2020). Evaluation of Deep Convolutional Neural Networks in Classifying Human Embryo Images Based on Their Morphological Quality. arXiv.","DOI":"10.1016\/j.heliyon.2021.e06298"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.rbmo.2020.07.003","article-title":"Embryo Ranking Intelligent Classification Algorithm (ERICA): Artificial intelligence clinical assistant predicting embryo ploidy and implantation","volume":"41","author":"Drakeley","year":"2020","journal-title":"Reprod. Biomed. Online"},{"key":"ref_4","first-page":"55","article-title":"The KidscoreTM D5 algorithm as an additional tool to morphological assessment and PGT-A in embryo selection: A time-lapse study","volume":"24","author":"Gazzo","year":"2020","journal-title":"JBRA Assist. Reprod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rbmo.2022.09.010","article-title":"Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?","volume":"46","author":"Kato","year":"2023","journal-title":"Reprod. Biomed. Online"},{"key":"ref_6","unstructured":"Vitrolife iDAScore-Intelligent Data Analysis for Embryo Evaluation. Available online: https:\/\/www.vitrolife.com\/our-products\/idascore-intelligent-data-analysis-for-embryo-evaluation\/."},{"key":"ref_7","first-page":"372","article-title":"Int\u00e9r\u00eat de l\u2019iDAScore (intelligent Data Analysis Score) dans la pratique quotidienne d\u2019un laboratoire de FIV pour la s\u00e9lection embryonnaire: R\u00e9sultats d\u2019une \u00e9tude pr\u00e9liminaire [Interest of iDAScore (intelligent Data Analysis Score) for Embryo Selection in Routine IVF Laboratory Practice: Results of a Preliminary Study]","volume":"51","author":"Sarandi","year":"2023","journal-title":"Gynecol. Obstet. Fertil. Senol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.rbmo.2022.08.098","article-title":"Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos","volume":"45","author":"Ezoe","year":"2022","journal-title":"Reprod. Biomed. Online"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ma, B.X., Zhao, G.N., Yi, Z.F., Yang, Y.L., Jin, L., and Huang, B. (2024). Enhancing clinical utility: Deep learning-based embryo scoring model for non-invasive aneuploidy prediction. Reprod. Biol. Endocrinol., 22.","DOI":"10.1186\/s12958-024-01230-w"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, C.I., Huang, C.C., Lee, T.H., Chen, H.H., Cheng, E.H., Lin, P.Y., Yu, T.N., Chen, C.I., Chen, C.H., and Lee, M.S. (2024). Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles. Reprod. Biol. Endocrinol., 22.","DOI":"10.1186\/s12958-024-01185-y"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cimadomo, D., Chiappetta, V., Innocenti, F., Saturno, G., Taggi, M., Marconetto, A., Casciani, V., Albricci, L., Maggiulli, R., and Coticchio, G. (2023). Towards automation in IVF: Pre-clinical validation of a deep learning-based embryo grading system during PGT-A cycles. J. Clin. Med., 12.","DOI":"10.3390\/jcm12051806"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Theilgaard Lassen, J., Fly Kragh, M., Rimestad, J., Nyg\u00e5rd Johansen, M., and Berntsen, J. (2023). Development and validation of deep learning-based embryo selection across multiple days of transfer. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-31136-3"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.fertnstert.2024.10.006","article-title":"External validation of a fully automated evaluation tool: A retrospective analysis of 68,471 scored embryos","volume":"123","author":"Bori","year":"2025","journal-title":"Fertil. Steril."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s10815-022-02562-5","article-title":"Correlation between an annotation-free embryo scoring system based on deep learning and live birth\/neonatal outcomes after single vitrified-warmed blastocyst transfer: A single-centre, large-cohort retrospective study","volume":"39","author":"Ueno","year":"2022","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1007\/s10815-021-02227-9","article-title":"PGT-A: Who and when? A systematic review and network meta-analysis of RCTs","volume":"38","author":"Simopoulou","year":"2021","journal-title":"J. Assist. Reprod. Genet."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/16\/8\/981\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:02Z","timestamp":1760034722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/16\/8\/981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":15,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["genes16080981"],"URL":"https:\/\/doi.org\/10.3390\/genes16080981","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]}}}