{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Dashti M"],"pubmed_abstract":["Despite advancements in assisted reproductive technology (ART), recurrent implantation failure (RIF) continues to pose a significant challenge to achieving pregnancy. We included 2,463 retrospective RIF patients with no gynecological and anatomical anomalies who were referred to a clinical immunologist and received targeted immunotherapies. Twenty-three variables were used to develop a deep learning (TabNet) model to predict live births. Statistical analyses were used to compare characteristics between live birth and implantation failure groups. Model performance was evaluated using a confusion matrix, the receiver operating characteristic (ROC) curve, and calibration plots. Our model showed an accuracy of 87.4% and an AUROC of 0.952. According to the model, when there were no missing inpu"],"journal":["Scientific reports"],"pagination":["42822"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12669587"],"repository":["biostudies-literature"],"pubmed_title":["Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study."],"pmcid":["PMC12669587"],"pubmed_authors":["Doustfateme S","Berjis K","Rahimifar S","Davoodi S","Yousefi M","Daraei M","Ardehaie RM","Danaii S","Amir A","Karimi S","Dashti M","Ghasemzadeh A","Jafarisavari Z","Bolouri N","Chakari-Khiavi F","Heris JA","Najdi N","Baharaghdam S"],"additional_accession":[]},"is_claimable":false,"name":"Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.","description":"Despite advancements in assisted reproductive technology (ART), recurrent implantation failure (RIF) continues to pose a significant challenge to achieving pregnancy. We included 2,463 retrospective RIF patients with no gynecological and anatomical anomalies who were referred to a clinical immunologist and received targeted immunotherapies. Twenty-three variables were used to develop a deep learning (TabNet) model to predict live births. Statistical analyses were used to compare characteristics between live birth and implantation failure groups. Model performance was evaluated using a confusion matrix, the receiver operating characteristic (ROC) curve, and calibration plots. Our model showed an accuracy of 87.4% and an AUROC of 0.952. According to the model, when there were no missing inpu","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-05T21:04:25.661Z","creation":"2026-05-21T03:13:02.853Z"},"accession":"S-EPMC12669587","cross_references":{"pubmed":["41326470"],"doi":["10.1038/s41598-025-27561-1"]}}