用视网膜图像实现跨设备、长时程的身份验证与检索，准确率超99%。
Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices
- 基于ConvNeXtV2和三元组损失训练512维度度量编码器
- 在3个数据集上召回率超97%，验证准确率接近100%
- 适用于长期随访医学影像库的身份纠错与管理
患者身份错误会损害纵向医疗记录、研究数据库及后续临床决策。本文提出一种视网膜生物识别系统，用于验证声称身份并从彩色眼底图像中检索正确身份。模型在21,851名患者的227,004张眼底图像上训练，覆盖多种成像设备和长达32.6年的随访。评估前，模型筛选出身份不一致图像，人工核查发现罗特特丹研究中0.588%、英国生物银行中0.259%、年龄相关眼病研究（AREDS）中0.164%存在误标。在剔除近似重复图像后的回溯验证中，三个数据集的AUROC分别达到0.9998、0.9997、0.9998；仅使用已有图像进行身份检索时，召回率@1分别为99.7%、97.2%、97.6%，且正确身份在前五名内占比不低于98.6%。性能在不同设备与长期随访下保持稳健，低图像质量与视网膜视野不一致是主要失败原因。结果表明视网膜解剖结构是可靠的生物特征信号，可用于保障纵向影像记录的完整性。
原文摘要 · Abstract (English)
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 UK Biobank and Age-Related Eye Disease Study (AREDS). Before evaluation, we used the model to screen for identity inconsistencies and manually adjudicated flagged images, identifying incorrect assignments in 0.588% of Rotterdam Study images, 0.259% of UK Biobank images, and 0.164% of AREDS images. In retrospective-only verification after removing near-duplicate images, the system achieved AUROCs of 0.9998, 0.9997, and 0.9998 in the Rotterdam Study, UK Biobank, and AREDS, respectively. For identity retrieval using only previously acquired images, Recall@1 was 99.7%, 97.2%, and 97.6%, respectively, from galleries averaging 4436-8510 identities; the correct identity appeared among the top five results in at least 98.6% of cases. Performance remained robust across imaging devices and long follow-up intervals, while lower image quality and inconsistent retinal fields accounted for most failures. These findings establish retinal anatomy as a durable biometric signal, useful for safeguarding the integrity of longitudinal imaging records.
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