Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices
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
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- PossiblePossibly related (embedding) · 48%Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images - Nature →
- LinkedLinked via arxiv author · 85%Jose D. Vargas-Quiros →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”
- LinkedLinked via arxiv author · 85%Dennis Bontempi →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”
- LinkedLinked via arxiv author · 85%Jeroen Vermeulen →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”
- LinkedLinked via arxiv author · 85%Bart Liefers →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”
- LinkedLinked via arxiv author · 85%Sven Bergmann →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”
- LinkedLinked via arxiv author · 85%Caroline C. W. Klaver →
“Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices”