Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-04-05
DOI
https://doi.org/10.1038/s41746-025-01582-6
Citations
11
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
22.63

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article

Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features

Zhenjun Tu, Miaohong Xu, Zizheng Cao, Zilian Wang et al.
11 citations
npj Digital Medicine
Pregnancy and preeclampsia studies
22.63
article

Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features

Zhenjun Tu, Miaohong Xu, Zizheng Cao, Zilian Wang, Duoru Lin, Dongni Wang, Lingyi Kong, Yuxuan Wu, Danli Shi, Weiling Hu, Yunjian Huang, Xiaohang Wu, Meimei Dongye, Haotian Lin, Shiyuan Chen, Lanqin Zhao, Zhenzhe Lin, Lixia Shen, Xiaohong Lin, Xulin Zhang, Xi Chen, Xun Wang, Dongyu Wang, Lixue Liu, Yihong Huang, Shengmei Zhou
article en
11 citations

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.

npj Digital MedicineVol. 8(1)
Hong Kong Polytechnic University (HK), Sun Yat-sen University (CN), Hainan Eye Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
National Natural Science Foundation of China, Sun Yat-sen University, Natural Science Foundation of Guangdong Province, National Key Research and Development Program of China
Zero hunger
Openalex Percentile: Top 0%
Pregnancy and preeclampsia studies
22.63
DOIOpen Access PDFOpenAlex
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