{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:49:19Z","timestamp":1768438159927,"version":"3.49.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.<\/jats:p>","DOI":"10.3389\/fimmu.2021.642167","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T04:54:42Z","timestamp":1617252882000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients"],"prefix":"10.3389","volume":"12","author":[{"given":"Chunyu","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongnu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dao Shen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun Kit","family":"Yip","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuye","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianghui","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lap Yan","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wai Lim","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenwei","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0165-0378(03)00083-4","article-title":"The immunological problem of pregnancy: 50 years with the hope of progress. A tribute to Peter Medawar","volume":"60","author":"Billington","year":"2003","journal-title":"J Reprod Immunol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1038\/ni.3131","article-title":"Immune mechanisms at the maternal-fetal interface: perspectives and challenges","volume":"16","author":"PrabhuDas","year":"2015","journal-title":"Nat Immunol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.jri.2017.10.045","article-title":"The role of decidual immune cells on human pregnancy","volume":"124","author":"Liu","year":"2017","journal-title":"J Reprod Immunol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1016\/j.fertnstert.2012.06.048","volume":"98","year":"2012","journal-title":"Fertil Steril"},{"key":"B5","doi-asserted-by":"publisher","first-page":"3036","DOI":"10.1093\/humrep\/del305","article-title":"Investigation and treatment of repeated implantation failure following IVF-ET","volume":"21","author":"Margalioth","year":"2006","journal-title":"Hum Reprod"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1007\/s10815-018-1266-6","article-title":"Are computational applications the \u201ccrystal ball\u201d in the IVF laboratory? The evolution from mathematics to artificial intelligence","volume":"35","author":"Simopoulou","year":"2018","journal-title":"J Assist Reprod Genet"},{"key":"B7","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.fertnstert.2018.10.030","article-title":"Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective","volume":"111","author":"Blank","year":"2019","journal-title":"Fertil Steril."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1007\/s10815-019-01498-7","article-title":"An artificial neural network for the prediction of assisted reproduction outcome","volume":"36","author":"Vogiatzi","year":"2019","journal-title":"J Assist Reprod Genet"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1093\/humrep\/12.7.1454","article-title":"The application of neural networks in predicting the outcome of in-vitro fertilization","volume":"12","author":"Kaufmann","year":"1997","journal-title":"Hum Reprod"},{"key":"B10","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s10815-019-01408-x","article-title":"Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018","volume":"36","author":"Curchoe","year":"2019","journal-title":"J Assist Reprod Genet"},{"key":"B11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J Microbiol Methods"},{"key":"B12","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-29694-9_14","article-title":"An overview of computational sparse models and their applications in artificial intelligence","volume-title":"Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence","author":"Deng","year":"2013"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1109\/JBHI.2015.2439242","article-title":"Subject-specific sparse dictionary learning for atlas-based brain MRI segmentation","volume":"19","author":"Roy","year":"2015","journal-title":"IEEE J Biomed Health Inform"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-0509-7-S4-S6","article-title":"Sparse representation approaches for the classification of high-dimensional biological data","volume":"7","author":"Li","year":"2013","journal-title":"BMC Syst Biol"},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-24888-2_15","article-title":"Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images","volume-title":"Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science","author":"Codella","year":"2015"},{"key":"B16","doi-asserted-by":"publisher","first-page":"403689","DOI":"10.1155\/2009\/403689","article-title":"Sparse representation for classification of tumors using gene expression data","volume":"2009","author":"Hang","year":"2009","journal-title":"J Biomed Biotechnol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future \u2014 big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N Engl J Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1017\/S0950268818001498","article-title":"Prediction of Shigellosis outcomes in Israel using machine learning classifiers","volume":"146","author":"Adamker","year":"2018","journal-title":"Epidemiol Infect"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1007\/s00521-018-3693-9","article-title":"A machine learning approach for prediction of pregnancy outcome following IVF treatment","volume":"32","author":"Hassan","year":"2018","journal-title":"Neural Comput Appl"},{"key":"B22","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1002\/uog.1061","article-title":"Expectant management of tubal ectopic pregnancy:Prediction of successful outcome using decision tree analysis","volume":"23","author":"Elson","year":"2004","journal-title":"Ultrasound Obstet Gynecol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.22074\/ijfs.2017.4882","article-title":"Predicting implantation outcome of in vitro fertilization and intracytoplasmic sperm injection using data mining techniques","volume":"11","author":"Hafiz","year":"2017","journal-title":"Int J Fertil Steril."},{"key":"B24","doi-asserted-by":"publisher","first-page":"149","DOI":"10.30699\/jogcr.3.4.12","article-title":"Prediction of clinical pregnancy occurrence after ICSI using decision tree and support vector machine methods","volume":"3","author":"Ghaeini","year":"2018","journal-title":"J Obstetr Gynecol Cancer Res"},{"key":"B25","doi-asserted-by":"publisher","first-page":"7970","DOI":"10.1038\/s41598-020-64512-4","article-title":"Machine learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice","volume":"10","author":"Bruno","year":"2020","journal-title":"Sci Rep"},{"key":"B26","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.media.2017.01.006","article-title":"Multi-resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images","volume":"37","author":"Li","year":"2017","journal-title":"Med Image Anal"},{"key":"B27","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41746-019-0089-x","article-title":"Towards a gamete matching platform: using immunogenetics and artificial intelligence to predict recurrent miscarriage","volume":"2","author":"Mora-Sanchez","year":"2019","journal-title":"NPJ Digit Med"},{"key":"B28","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1111\/aji.12217","article-title":"Experience in IVIg therapy for selected women with recurrent reproductive failure and NK cell expansion","volume":"71","author":"Ramos-Medina","year":"2014","journal-title":"Am J Reprod Immunol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1093\/humrep\/dev268","article-title":"Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool","volume":"31","author":"Dhillon","year":"2016","journal-title":"Hum Reprod."},{"key":"B30","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.advms.2017.02.001","article-title":"How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis","volume":"62","author":"Milewski","year":"2017","journal-title":"Adv Med Sci."},{"key":"B31","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.fertnstert.2016.12.005","article-title":"Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF\/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF\/ICSI single-embryo transfers","volume":"107","author":"Vaegter","year":"2017","journal-title":"Fert Steril."},{"key":"B32","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1186\/s12967-019-2062-5","article-title":"Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method","volume":"17","author":"Qiu","year":"2019","journal-title":"J Transl Med"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1007\/s10815-020-01908-1","article-title":"Machine learning vs. classic statistics for the prediction of IVF outcomes","volume":"37","author":"Barnett-Itzhaki","year":"2020","journal-title":"J Assist Reprod Genet"},{"key":"B34","first-page":"399","article-title":"Learning fast approximations of sparse coding","volume-title":"Proceedings of the 27th International Conference on International Conference on Machine Learning","author":"Gregor","year":"2010"},{"key":"B35","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.rbmo.2013.05.018","article-title":"Implantation in assisted reproduction: a look at endometrial receptivity","volume":"27","author":"Fatemi","year":"2013","journal-title":"Reprod Biomed Online"},{"key":"B36","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1530\/REP-16-0574","article-title":"A comparison of transcriptomic profiles in endometrium during window of implantation between women with unexplained recurrent implantation failure and recurrent miscarriage","volume":"153","author":"Huang","year":"2017","journal-title":"Reproduction"},{"key":"B37","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1093\/humupd\/dmn013","article-title":"Bidirectional effects on autoimmunity and reproduction","volume":"14","author":"Cervera","year":"2008","journal-title":"Hum Reprod Update"},{"key":"B38","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/S0015-0282(99)00498-7","article-title":"Antiphospholipid antibodies and in vitro fertilization success: a meta-analysis","volume":"73","author":"Hornstein","year":"2000","journal-title":"Fertil Steril"},{"key":"B39","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1111\/j.8755-8920.2001.450202.x","article-title":"The association between anti-thyroid antibodies and pregnancy loss","volume":"45","author":"Matalon","year":"2001","journal-title":"Am J Reprod Immunol"},{"key":"B40","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1093\/humrep\/15.3.545","article-title":"Increased prevalence of thyroid antibodies in euthyroid women with a history of recurrent in-vitro fertilization failure","volume":"15","author":"Bussen","year":"2000","journal-title":"Hum Reprod"},{"key":"B41","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/BF03347388","article-title":"Euthyroid women with autoimmune disease undergoing assisted reproduction technologies: the role of autoimmunity and thyroid function","volume":"30","author":"Negro","year":"2007","journal-title":"J Endocrinol Invest"},{"key":"B42","doi-asserted-by":"publisher","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol"},{"key":"B43","doi-asserted-by":"publisher","first-page":"56668","DOI":"10.3791\/56668","article-title":"A neural network-based identification of developmentally competent or incompetent mouse fully-grown oocytes","volume":"133","author":"Cavalera","year":"2018","journal-title":"J Vis Exp"},{"key":"B44","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1093\/biolre\/iox120","article-title":"CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns","volume":"97","author":"Goodson","year":"2017","journal-title":"Biol Reprod"},{"key":"B45","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.fertnstert.2017.10.036","article-title":"Time-lapse algorithms and morphological selection of day-5 embryos for transfer: a preclinical validation study","volume":"109","author":"Storr","year":"2018","journal-title":"Fertil Steril"},{"key":"B46","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s11517-015-1299-2","article-title":"Estimating the chance of success in IVF treatment using a ranking algorithm","volume":"53","author":"Guvenir","year":"2015","journal-title":"Med Biol Eng Comput"},{"key":"B47","doi-asserted-by":"publisher","first-page":"138","DOI":"10.32474\/MAMS.2020.02.000138","article-title":"Deep learning limitations and flaws","volume":"2","author":"Zohuri","year":"2020","journal-title":"Mod App Matrl Sci."}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2021.642167\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T04:54:48Z","timestamp":1617252888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2021.642167\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":47,"alternative-id":["10.3389\/fimmu.2021.642167"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2021.642167","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,1]]},"article-number":"642167"}}