ArticleNPJ digital medicine2025
Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Retinal imaging in pre-eclamptic pregnancy: systematic review.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Pooled it
- Epigenetic landscape of placental tissue in preeclampsia: a systematic review of DNA methylation profiles.BMC pregnancy and childbirth · 2025Pooled it
- Decoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI.Nature biotechnology · 2026Article
- Digital and AI-assisted approaches across the preeclampsia care pathway: a scoping review.AJOG global reports · 2026Article
- Hypertensive retinopathy in pre-eclampsia and its association with disease severity and neonatal outcomes: A retrospective cohort study.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- A first-trimester mechanistic framework integrating three Physiopathologic biomarker domains for pre-eclampsia classification.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Associations of preconception retinal microvasculature with lipid profiles from mid-pregnancy to three months postpartum.NPJ cardiovascular health · 2026Article
- From concept to clinic: emerging paradigms and novel insights into liquid biopsy.Cancer biology & medicine · 2026Article
- Article
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial Intelligence.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging retinal vascular features in non-invasive, early diagnosis of preeclampsia.NPJ digital medicine · 2025Article
- A multimodal visual-language foundation model for computational ophthalmology.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
26 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.