Machine Learning–Based Prediction of Large-for-Gestational-Age Infants in Mothers With Gestational Diabetes Mellitus

Abstract

Abstract Context Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited. Objective We aimed to develop and validate an artificial intelligence (AI)-based model to determine the probability of women with GDM giving birth to LGA infants during pregnancy using CGM measurements together with demographic data and metabolic indicators. Methods A total of 371 women with GDM from a prospective cohort at a university hospital were included. CGM was performed during 20 to 34 gestational weeks, and glycemic fluctuations were evaluated and visualized in women with GDM who gave birth to LGA and non-LGA infants. A convolutional neural network (CNN)-based fusion model was developed to predict LGA. Comparisons among the novel fusion model and 3 conventional models were made using the area under the receiver operating characteristic curve (AUCROC) and accuracy. Results Overall, 76 (20.5%) out of 371 GDM women developed LGA neonates. The visualized 24-hour glucose profiles differed at midmorning. This difference was consistent among subgroups categorized by pregestational body mass index, therapeutic protocol, and CGM administration period. The AI-based fusion prediction model using 24-hour CGM data and 15 clinical variables for LGA prediction (AUCROC 0.852; 95% CI, 0.680-0.966; accuracy 84.4%) showed superior discriminative power compared with the 3 classic models. Conclusion We demonstrated better performance in predicting LGA infants among women with GDM using the AI-based fusion model. The characteristics of the CGM profiles allowed us to determine the appropriate window for intervention.