Novel Clinically Validated Machine Learning Model for Early Pregnancy Loss in Recurrent Spontaneous Abortion: Integrating Serum Autoantibodies and Ultrasonic Parameters

Objective: To explore the correlation between autoantibodies, ultrasonic endometrial receptivity parameters and early miscarriage in recurrent spontaneous abortion (RSA) patients during subsequent pregnancies, and to establish and validate a predictive model for early miscarriage. Methods: A retrospective analysis was conducted on RSA patients who visited Xi'an People's Hospital from January 2019 to December 2024. Patients were randomly divided into a training set (70%, n=412) and a validation set (30%, n=177). Baseline data, serum autoantibodies (anti-β2-glycoprotein 1 antibody [aβ2-GP1], thyroglobulin antibody [TgAb], anti-sperm antibody [AsAb], anti-cardiolipin antibody [ACA]) and ultrasonic parameters (resistance index [RI], endometrial thickness, endometrial volume, vascularization index [VI], vascularization flow index [VFI]) were collected. Multiple machine learning models (logistic regression [LR], XGBoost, random forest, etc.) were developed. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, and other metrics. A nomogram was constructed based on the optimal model. Results: The abortion subgroup had significantly higher positive rates of aβ2-GP1, TgAb, ACA and RI, but lower endometrial thickness, endometrial volume, VI and VFI than the normal subgroup (all P