aceDecoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI
Hypertensive disorders of pregnancy arise from early placental and systemic vascular dysfunction, yet current first-trimester screening remains imprecise, biologically opaque and logistically challenging. Here we present Visionary AI, an interpretable platform that integrates retinal imaging with graph-based vascular modeling to predict hypertensive disorders before clinical onset. Visionary AI converts retinal images into topological and geometric representations of microvascular structure, enabling risk predic…
An interpretable retinal-imaging model using vascular topology and geometry predicted preeclampsia in a prospective cohort and transferred to an independent cohort, outperforming clinical and deep learning benchmarks.
The model's performance was lower in the external validation cohort (AUC 0.81) than in development (AUC 0.91), and the abstract reports no evidence on clinical outcomes or use in practice.
Evidence
- Peer-reviewedNature Biotechnology2026-10-06
How should this claim be treated?
Truvace Impact Record TRV-2026-1799, v1: “Decoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI.” Truvace, 2026-10-18. /record/TRV-2026-1799 (accessed at citation time). sha256 47bb41dc91b92e92…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1799 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.