biostudies-literatureUnknown15(1)Dashti MDespite 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 inpuScientific reports42822https://www.ebi.ac.uk/biostudies/studies/S-EPMC12669587biostudies-literatureImmunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.PMC12669587Doustfateme SBerjis KRahimifar SDavoodi SYousefi MDaraei MArdehaie RMDanaii SAmir AKarimi SDashti MGhasemzadeh AJafarisavari ZBolouri NChakari-Khiavi FHeris JANajdi NBaharaghdam SfalseImmunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.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 inpu2025-01-01T00:00:00Z2025 Dec2026-06-05T21:04:25.661Z2026-05-21T03:13:02.853ZS-EPMC126695874132647010.1038/s41598-025-27561-1