SOURCE-LINKED INTELLIGENCE

Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Patient identity errors can compromise longitudinal medical records, research databases, and downstream clinical decisions. We present a retinal biometric system for verifying claimed identities and retrieving the correct identity from color fundus images. We trained a 512-dimensional metric-learning encoder combining a ConvNeXtV2 backbone with ArcFace and triplet losses on 227,004 images from 21,851 patient-eye identities in the Rotterdam Study, spanning multiple imaging devices and up to 32.6 years of follow-up. The system was evaluated on held-out Rotterdam Study data and externally on the

Read original source ↗ Open in workspace

recordType

paper

region

Global