Ensemble learning for fetal ultrasound and maternal-fetal data to predict mode of delivery after labor induction

Abstract

Providing adequate counseling on mode of delivery after induction of labor (IOL) is of utmost importance. Various AI algorithms have been developed for this purpose, but rely on maternal-fetal data, not including ultrasound (US) imaging. We used retrospectively collected clinical data from 808 subjects submitted to IOL, totaling 2024 US images, to train AI models to predict vaginal delivery (VD) and cesarean section (CS) outcomes after IOL. The best overall model used only clinical data (F1-score: 0.736; positive predictive value (PPV): 0.734). The imaging models employed fetal head, abdomen and femur US images, showing limited discriminative results. The best model used femur images (F1-score: 0.594; PPV: 0.580). Consequently, we constructed ensemble models to test whether US imaging could enhance the clinical data model. The best ensemble model included clinical data and US femur images (F1-score: 0.689; PPV: 0.693), presenting a false positive and false negative interesting trade-off. The model accurately predicted CS on 4 additional cases, despite misclassifying 20 additional VD, resulting in a 6.0% decrease in average accuracy compared to the clinical data model. Hence, integrating US imaging into the latter model can be a new development in assisting mode of delivery counseling.

Figure 1

The ROCs for prediction of mode of delivery for tabular data (a) and image-based data (b). ROC, receiver operating characteristic.

Figure 2

Confusion matrices on (a) the best tabular data model AdaBoost and (b) the best image-based model of the femur, (c) head and (d) abdomen are shown. Confusion matrix depicting in reading order from left to right, top to bottom: true-negative, false-negative, false-positive and true-positive rates.

Figure 3

Process involved in the establishment of the ensemble models. The three image-based models (Inception head, abdomen and femur) were associated with the best tabular data model, AdaBoost in three different ways. Green box: Image-based model, using the CNN Inception model of the femur, abdomen and head; Orange box: AdaBoost tabular data model with Inception models of the femur, abdomen and head; and Blue box: the Final classification model, which consists of the AdaBoost tabular data model and the Inception model of the femur, which is the ensemble model which provides the best metrics.

Figure 4

The ROC curves for prediction of mode of delivery for the ensemble models and their comparison with the ROC curves of the Adaboost and Inception femur models. ROC, receiver operating characteristic.

Figure 5

Confusion matrices on the following ensemble models: (a) image-based model, (b) AdaBoost and Inception models of the femur, head and abdomen (majority voting) (c) the final classification model (majority voting). Confusion matrix depicting in reading order from left to right, top to bottom: true-negative, false-negative, false-positive and true-positive rates.