Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.

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

SUBMITTER: Dashti M 

PROVIDER: S-EPMC12669587 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature