利用视网膜血管特征进行子痫前期的无创早期诊断。
Leveraging retinal vascular features in non-invasive, early diagnosis of preeclampsia.
作者信息
Heydari Kimia, Enichen Elizabeth J, Li Ben, Kvedar Joseph C
机构信息
Harvard Medical School, Boston, MA, USA.
Division of Vascular Surgery, University of Toronto, Toronto, ON, Canada.
出版信息
NPJ Digit Med. 2025 Jul 10;8(1):422. doi: 10.1038/s41746-025-01856-z.
Abstract
Wu et al.’s recent article, “Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features,” documents significant differences in retinal vascular features among women who develop preeclampsia and those with normotensive pregnancies. These findings provide evidence that retinal screening has the potential to be used as a low-cost, non-invasive screening strategy to support the earlier detection, prevention, and treatment of preeclampsia.
摘要
吴等人最近的文章《利用视网膜血管特征对妊娠期先兆子痫进行无创早期预测》记录了发生先兆子痫的女性与血压正常的孕妇在视网膜血管特征方面存在显著差异。这些发现表明，视网膜筛查有可能作为一种低成本、无创的筛查策略，以支持先兆子痫的早期检测、预防和治疗。
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本文引用的文献
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NPJ Digit Med. 2025 Apr 5;8(1):188. doi: 10.1038/s41746-025-01582-6.
2
A novel model for retinal imaging in the diagnosis of Alzheimer's disease.一种用于阿尔茨海默病诊断的视网膜成像新模型。
NPJ Digit Med. 2025 Jan 9;8(1):19. doi: 10.1038/s41746-025-01437-0.
3
Harnessing women's full reproductive history in assessing cardiovascular risk.在评估心血管风险时利用女性完整的生殖史。
Paediatr Perinat Epidemiol. 2024 Mar;38(3):268-270. doi: 10.1111/ppe.13064. Epub 2024 Mar 7.
4
Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.通过对常规收集的电子病历数据中的妊娠轨迹进行建模来改善子痫前期风险预测。
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