Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

Abstract

Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.

Keywords: artificial intelligence; assisted reproductive technology; recurrent reproductive failure; reproductive immunology; sparse coding.

Figure 1

Autoantibodies panel performance of sparse coding in predicting pregnancy outcomes at different pregnancy periods. (A) ROC plot of the training data set. (B) ROC plot of the testing data set.

Figure 2

Peripheral immunology panel performance of sparse coding in predicting pregnancy outcomes at different pregnancy periods. (A) ROC plot of the training data set. (B) ROC plot of the testing data set.

Figure 3

Endometrial immunology panel performance of sparse coding in predicting pregnancy outcomes at different pregnancy periods. (A) ROC plot of the training data set. (B) ROC plot of the testing data set.

Figure 4

Combination of immunology-related panels (autoantibodies, peripheral immunology, and endometrial immunology) performance of sparse coding in predicting pregnancy outcomes at different pregnancy periods. (A) ROC plot of the training data set. (B) ROC plot of the testing data set.

Figure 5

Combination of immunology and IVF-related panels (autoantibodies, peripheral immunology, endometrial immunology, basic patient characteristic, hormone level, and embryo parameter) performance of sparse coding in predicting pregnancy outcomes at different pregnancy periods. (A) ROC plot of the training data set. (B) ROC plot of the testing data set.