Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features

• Published in: NPJ digital medicine Vol. 8; no. 1; pp. 188 - 9
• Main Authors: Wu, Yuxuan, Shen, Lixia, Zhao, Lanqin, Lin, Xiaohong, Xu, Miaohong, Tu, Zhenjun, Huang, Yihong, Kong, Lingyi, Lin, Zhenzhe, Lin, Duoru, Liu, Lixue, Wang, Xun, Cao, Zizheng, Chen, Xi, Zhou, Shengmei, Hu, Weiling, Huang, Yunjian, Chen, Shiyuan, Dongye, Meimei, Zhang, Xulin, Wang, Dongni, Shi, Danli, Wang, Zilian, Wu, Xiaohang, Wang, Dongyu, Lin, Haotian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/53/2423; 692/699/75/243/793; Artificial intelligence; Biomarkers; Biomedicine; Biotechnology; Clinical medicine; Gestational age; Hospitals; Hypertension; Kidney diseases; Machine learning; Medicine; Medicine & Public Health; Mortality; Obstetrics; Ophthalmology; Overweight; Preeclampsia; Pregnancy; Prenatal care; Regression analysis; Risk factors; Womens health; Hong Kong China; China

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 + Ophthalm...