ArticleScientific reports2025
Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Multivariable AI-based analysis of immune-lifestyle patterns associated with recurrent pregnancy loss: an exploratory retrospective study.Scientific reports · 2026Article
- Precision Microbial Therapeutics for Infertility: Next-Generation Probiotics, Engineered Biologics and Translational Pathways.Microbial biotechnology · 2026Review
- Classification of recurrent implantation failure patients using peripheral blood immunological and metabolic markers.BioImpacts : BI · 2026Article
- Endometrial immune dysregulation shapes CD8Frontiers in immunology · 2026Article
Corrections and comments
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Authors and funding
18 authors.
Funding
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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 input variables, the most important features were age, Th1/Th2 ratio, BMI, anti-thyroid peroxidase (anti-TPO), antinuclear antibodies (ANA), anti-dsDNA, and anti-tissue transglutaminase (anti-TTG), respectively. In conclusion, the TabNet model yielded strong performance in predicting live births in RIF patients using a combination of 23 variables. This model can help improve understanding of the underlying mechanism of implantation failure and stratify patients who may benefit from immune modulation interventions.
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