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Immunological risk factors for recurrent implantation failure using a deep learning model: a multicenter
Mohsen Dashti1,2, Afsaneh Ghasemzadeh1,2, Sare Doustfateme3,4
1Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Scientific Reports
|December 1, 2025
Summary
A deep learning model accurately predicts live births in patients with recurrent implantation failure (RIF). Key factors include age, BMI, and immune markers, aiding personalized treatment strategies for infertility.
Area of Science:
- Reproductive Immunology
- Artificial Intelligence in Medicine
Background:
- Recurrent implantation failure (RIF) remains a significant obstacle in assisted reproductive technology (ART).
- Understanding the immunological factors contributing to RIF is crucial for improving pregnancy outcomes.
Purpose of the Study:
- To develop and validate a deep learning model (TabNet) for predicting live births in RIF patients.
- To identify key predictive variables for live births in RIF, including immunological markers.
Main Methods:
- Retrospective analysis of 2,463 RIF patients without gynecological anomalies.
- Development of a TabNet deep learning model using 23 variables.
- Statistical comparison of patient characteristics and model performance evaluation using ROC curves and calibration plots.
Main Results:
- The TabNet model achieved 87.4% accuracy and an AUROC of 0.952 in predicting live births.
- Important predictive features identified by the model include age, Th1/Th2 ratio, BMI, anti-thyroid peroxidase (anti-TPO), antinuclear antibodies (ANA), anti-dsDNA, and anti-tissue transglutaminase (anti-TTG).
Conclusions:
- The TabNet model demonstrates strong predictive performance for live births in RIF patients.
- This AI-driven approach can enhance the understanding of RIF mechanisms and guide patient stratification for immunotherapies.
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