Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features

Yuxuan Wu, Lixia Shen, Lanqin Zhao, Xiaohong Lin, Miaohong Xu, Zhenjun Tu, Yihong Huang, Lingyi Kong, Zhenzhe Lin, Duoru Lin, Lixue Liu, Xun Wang, Zizheng Cao, Xi Chen, Shengmei Zhou, Weiling Hu, Yunjian Huang, Shiyuan Chen, Meimei Dongye, Xulin Zhang et al. (6 additional authors not shown)

Abstract: Preeclampsia (PE), a severe hypertensive disorder during pregnancy, significantly contributes to maternal and neonatal mortality. Existing prediction biomarkers are often invasive and expensive, hindering their widespread application. This study introduces PROMPT (Preeclampsia Risk factor + Ophthalmic data + Mean arterial pressure Prediction Test), an AI-driven model leveraging retinal photography for PE prediction, registered at ChiCTR (ChiCTR2100049850) in August 2021. Analyzing 1812 pregnancies before 14 gestational weeks, we extracted retinal parameters using a deep learning system. The PROMPT achieved an AUC of 0.87 (0.83–0.90) for PE prediction and 0.91 (0.85–0.97) for preterm PE prediction using machine learning, significantly outperforming the baseline model (p < 0.001). It also improved detection of severe adverse pregnancy outcomes from 35% to 41%. Economically, PROMPT was estimated to avert 1809 PE cases and saved over $50 million per 100,000 screenings. These results position PROMPT as a non-invasive and cost-effective tool for prenatal care, especially valuable in low- and middle-income countries.