Deep Learning Enables Early-Stage Prediction of Preterm Birth Using Vaginal Microbiota

Objective: Preterm birth (PTB) is one of the leading issues concerning infant health and is a problem that plagues all parts of the world. Vaginal microbial communities have recently garnered attention in the context of PTB, however, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically and geographically diverse populations. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. In this study, we develop a neural controlled differential equations (CDEs) based framework for identifying early PTBs in racially diverse cohorts from irregularly sampled vaginal microbial abundance data. Methods: We obtained relative abundances of microbial species within vaginal microbiota using 16S rRNA sequences obtained from vaginal swabs at various stages of pregnancy. We employed a recently introduced deep learning paradigm known as ``Neural CDEs" to predict PTBs. This method, previously unexplored, analyzes irregularly sampled microbial abundance profiles in a time-series format. Results: Our framework is able to identify signatures in the temporally evolving vaginal microbiome during trimester~2 and can predict incidences of PTB (mean test set ROC-AUC = 0.81, accuracy = 0.75, f1 score = 0.71) significantly better than traditional ML classifiers, thus enabling effective early-stage PTB risk assessment. Conclusion and Significance: Our method is able to differentiate between term and preterm outcomes with a substantial accuracy, despite being trained using irregularly sampled microbial abundance profiles, thus overcoming the limitations of traditional time-series modeling methods.