Evidence map›Paper›PMID 41358297›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial Intelligence.

Srilaxmi Bearelly, Cassandra Areff, Matthew K Hoffman, Daniel Sunko, Andrew F Laine, Bin Choi, Hanna Rodriguez Coleman, Bianca Cordazzo Vargas, Chloe Y Li, Jennifer Dawkins and 6 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Srilaxmi BearellyDepartment of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Cassandra AreffInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Matthew K HoffmanDivision of Obstetrics & Gynecology, ChristianaCare, Newark, DE, USA.
Daniel SunkoInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Andrew F LaineDepartment of Biomedical Engineering, Columbia University, New York, NY, USA.
Bin ChoiInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Hanna Rodriguez ColemanDepartment of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Bianca Cordazzo VargasInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Chloe Y LiDepartment of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Jennifer DawkinsInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Emily AmirDepartment of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Lisa A HarkDepartment of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA.
Whitney A BookerDepartment of Obstetrics & Gynecology, Columbia University Irving Medical Center, New York, NY, USA.
April JauhalInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Ronald J WapnerDepartment of Obstetrics & Gynecology, Columbia University Irving Medical Center, New York, NY, USA.
Liat ShenhavInstitute for Systems Genetics, NYU Grossman School of Medicine, New York University, New York, NY, USA.ORCID 0000-0003-1708-6050

Funding

Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
NCATS NIH HHS UL1 TR001873
6 · The paper itself

Abstract

Pregnancy orchestrates a rare physiological transformation across vascular, immune, and metabolic systems. When this dynamic balance is disrupted - as in hypertensive disorders of pregnancy - the consequences can be life-threatening, spanning maternal mortality, fetal growth restriction, and elevated long-term cardiovascular risk. Despite clear links to early placental dysfunction and systemic endothelial disruption, current screening remains clinically imprecise, biologically opaque, and logistically challenging. A shift is urgently needed - from detecting maternal complications late in gestation to understanding how pregnancy reshapes vascular physiology systemically and how this remodeling may go awry. Here we present Visionary AI, an artificial intelligence platform that integrates ultra-widefield retinal imaging (200°) with biologically grounded vascular modeling to predict hypertensive disorders of pregnancy early in gestation. Unlike prior approaches that rely on generic deep learning models and clinical inputs, Visionary AI constructs an interpretable, graph-based representation of the maternal retinal vasculature and applies topological and geometric analysis to identify condition-specific microvascular signatures. In a prospective multiethnic U.S. cohort of 1,267 pregnancies, Visionary AI achieved high predictive performance for preeclampsia (AUC = 0.90), early-onset (AUC = 0.93), and severe preeclampsia (AUC = 0.89), outperforming current clinical paradigms. It also generalized to predict gestational hypertension (AUC = 0.91) and chronic hypertension (AUC = 0.90). Topological and geometric analyses of the vasculature revealed distinct and interpretable remodeling patterns across subtypes of hypertensive disorders of pregnancy, offering mechanistic insight into their divergent pathophysiology. These results position the maternal retina as a minimally invasive, high-fidelity biosensor of early systemic vascular health and establish Visionary AI as a clinically actionable, biologically grounded diagnostic framework with potential for broad global scalability.

Identifiers

PMID41358297
PMCPMC12676550

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.