A multimodal deep learning model for predicting severe hemorrhage in placenta previa

Abstract

Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using MRI T2-weighted image of the placenta and tabular data consisting of patient demographics and preoperative blood examination data, a multimodal deep learning model was constructed to predict cases of intraoperative blood loss > 2000 ml. We evaluated the prediction performance of the model by comparing it with that of two machine learning methods using only tabular data and MRI images, as well as with that of two human expert obstetricians. Among the enrolled 48 patients, 26 (54.2%) lost > 2000 ml of blood and 22 (45.8%) lost < 2000 ml of blood. Multimodal deep learning model showed the best accuracy of 0.68 and AUC of 0.74, whereas the machine learning model using tabular data and MRI images had a class accuracy of 0.61 and 0.53, respectively. The human experts had median accuracies of 0.61. Multimodal deep learning models could integrate the two types of information and predict severe hemorrhage cases. The model might assist human expert in the prediction of intraoperative hemorrhage in the case of placenta previa.