(1144) Predicting preeclampsia and gestational hypertension using the vasculature of the eye

Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. Notably, the initial vascular alterations leading to preeclampsia occur in the placenta and maternal vasculature well before symptoms develop. This observation is key to novel diagnostics. Here we evaluate, for the first time, changes in the retinal vasculature occurring prior to the patient becoming symptomatic and use an Artificial Intelligence (AI) framework to develop and test a vascularity based predictive algorithm.

Study Design:

Recent advancements in ocular imaging now provide high-resolution, cheap, and non-invasive imaging of the retina. Using the Optos Daytona machine, we collected retinal images from 300 pregnant women at 1^st, 2^nd and 3^rd trimester visits. We further developed an AI framework, combining topological data analysis and deep learning termed DVT-Net (Deep Vascular Topology Network), suitable to characterize the topological and geometrical changes in the retinal vasculature to predict preeclampsia (Figure 1).

Results:

From the cohort of 300 pregnancies, 12 women were diagnosed with preeclampsia, 40 with gestational hypertension and 248 had neither. Using images from the first and second trimesters, our computational framework (DVT-Net) and a 10-fold cross validation, we achieved an accuracy of 0.813 (95% CI [0.76, 0.91]) in predicting the above-mentioned pregnancy-disorders.

Conclusion:

Our approach provides a nuanced description of the retinal vasculature topology in normal and hypertensive pregnancies and serves as an accessible and sensitive imaging surrogate of placental vasculature changes. This may allow early prediction of preeclampsia and gestational hypertension.