An Evaluation of Cervix Maturity by Machine Learning and Ultrasound Images

Abstract

Abstract Background: To evaluate the improvement of evaluation accuracy of cervix maturity by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. Methods: The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in 2019-2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time, measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost (eXtreme Gradient Boosting), CatBoost (an implementation of Gradient Boosted Decision Trees), and Random forest (RF). We considered the mean value of each scoring group of the traditional Bishop scoring method as the predicted value and calculated the mean square error of the real value and the predicted value for each data, considering the root-mean-squared error (RMSE) as the criterion to evaluate the accuracy of the model. We made a significant t-test on RMSE between the machine learning model and the traditional Bishop score. Results: The mean absolute error (MAE) of the prediction result of Bishop scoring method was 19.45 hours, and the RMSE was 24.56 hours. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 hours and the RMSE was 16.98 hours. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was <0.01. Conclusion: In the evaluation of cervix maturity, the results of machine learning method are more objective and significantly accurate compared with the traditional Bishop scoring method. The machine learning method is a better predictor of cervix maturity than the traditional Bishop method.