An evaluation of cervical maturity for Chinese women with labor induction by machine learning and ultrasound images.

BACKGROUND: To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction 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 between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), 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, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. RESULTS: The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. 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 h and the RMSE was 16.98 h. 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 cervical 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 cervical maturity than the traditional Bishop method.

Correlation of An Ultrasonic Scoring System and Intraoperative Blood Loss in Placenta Accreta Spectrum Disorders: A Retrospective Cohort Study.

OBJECTIVE: This study aims to investigate the correlation of an ultrasonic scoring system with intraoperative blood loss (IBL) in placenta accreta spectrum (PAS) disorders. METHODS: A retrospective cohort study was conducted between January 2015 and November 2019. Clinical data for patients with PAS have been obtained from medical records. Generalized additive models were used to explore the nonlinear relationships between ultrasonic scores and IBL. Logistic regressions were used to determine the differences in the risk of IBL ≥ 1,500 mL among groups with different ultrasonic scores. RESULTS: A total of 332 patients participated in the analysis. Generalized additive models showed a significant positive correlation between score and blood loss. The amount of IBL was increased due to the rise in the ultrasonic score. All cases were divided into three groups according to the scores (low score group: ≤ 6 points, n = 147; median score group: 7-9 points, n = 126; and high score group: ≥ 10 points, n = 59). Compared with the low score group, the high score group showed a higher risk of IBL ≥ 1,500 mL [odds ratio, 15.09; 95% confidence interval (3.85, 59.19); P ≤ 0.001] after a multivariable adjustment. CONCLUSIONS: The risk of blood loss equal to or greater than 1,500 mL increases further when ultrasonic score greater than or equal to 10 points, the preparation for transfusion and referral mechanism should be considered.