โก Quick Summary
A recent study utilized a deep learning model to identify immunological risk factors associated with recurrent implantation failure (RIF) in 2,463 patients. The model achieved an impressive accuracy of 87.4% and an AUROC of 0.952, paving the way for improved patient stratification and targeted immunotherapy.
๐ Key Details
- ๐ Dataset: 2,463 RIF patients with no gynecological or anatomical anomalies
- ๐งฉ Features used: 23 immunological and clinical variables
- โ๏ธ Technology: Deep learning model (TabNet)
- ๐ Performance: Accuracy 87.4%, AUROC 0.952
๐ Key Takeaways
- ๐ฌ Immunological factors play a crucial role in RIF, influencing the chances of live birth.
- ๐ค The TabNet model effectively predicts live births using a combination of clinical and immunological data.
- ๐ Key predictors include age, Th1/Th2 ratio, BMI, and various autoantibodies.
- ๐ Multicenter study enhances the reliability of findings across diverse patient populations.
- ๐ก Potential for personalized medicine through targeted immunotherapy based on model predictions.
- ๐ Study published in Scientific Reports, highlighting the importance of ongoing research in reproductive health.
๐ Background
Recurrent implantation failure (RIF) remains a significant hurdle in assisted reproductive technology (ART), affecting many couples trying to conceive. Despite advancements in ART, understanding the underlying immunological factors contributing to RIF is crucial for improving patient outcomes. This study aims to bridge that gap by employing a deep learning approach to analyze a large cohort of RIF patients.
๐๏ธ Study
Conducted as a multicenter retrospective cohort study, this research included 2,463 patients who experienced RIF but had no identifiable gynecological or anatomical issues. These patients were referred to clinical immunologists and received targeted immunotherapies. The study focused on developing a predictive model using 23 variables to assess the likelihood of achieving a live birth.
๐ Results
The deep learning model, TabNet, demonstrated remarkable performance with an accuracy of 87.4% and an AUROC of 0.952. The analysis revealed that the most significant predictors of live birth included age, Th1/Th2 ratio, BMI, and specific autoantibodies such as anti-thyroid peroxidase (anti-TPO) and antinuclear antibodies (ANA). These findings underscore the importance of immunological assessments in managing RIF cases.
๐ Impact and Implications
The implications of this study are profound. By leveraging a deep learning model to identify key immunological factors, healthcare providers can better stratify patients who may benefit from immune modulation interventions. This approach not only enhances our understanding of implantation failure mechanisms but also opens avenues for personalized treatment strategies in reproductive medicine.
๐ฎ Conclusion
This study highlights the potential of deep learning technologies in predicting live births among RIF patients by analyzing a comprehensive set of immunological factors. As we continue to explore the intersection of technology and reproductive health, the findings from this research could lead to more effective interventions and improved outcomes for couples facing the challenges of RIF. The future of reproductive medicine looks promising with such innovative approaches!
๐ฌ Your comments
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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 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.
Author: [‘Dashti M’, ‘Ghasemzadeh A’, ‘Doustfateme S’, ‘Daraei M’, ‘Danaii S’, ‘Najdi N’, ‘Berjis K’, ‘Heris JA’, ‘Chakari-Khiavi F’, ‘Karimi S’, ‘Rahimifar S’, ‘Davoodi S’, ‘Baharaghdam S’, ‘Bolouri N’, ‘Jafarisavari Z’, ‘Ardehaie RM’, ‘Amir A’, ‘Yousefi M’]
Journal: Sci Rep
Citation: Dashti M, et al. Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study. Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter retrospective cohort study. 2025; 15:42822. doi: 10.1038/s41598-025-27561-1