"Radiobiometrics": Deep-learning radiograph biometrics for patient identification.

When reporting on radiology follow-up examinations, radiologists should ensure that follow-up images and reference images are from the same patient to prevent misidentification errors. This is a nontrivial task when accounting for changes in the patient's condition and differences in image acquisition. Thus, we developed a system for automatic patient identification from radiographs using convolutional neural networks. Using deep metric learning, we trained multiple models to match radiographs of the chest, knees, pelvis, and hands. We also trained a model to match chest radiographs of multiple viewpoints (frontal and lateral). All models achieved over 0.98 true positive rate (TPR) at a 0.001 false positive rate (FPR), and over 0.96 rank-1 TPR on internal test datasets. The multi-view chest-radiograph CNN maintained over 0.98 TPR and 0.97 rank-1 TPR when matching frontal radiographs with lateral radiographs. Our work demonstrates the potential of radiographs as a biometric modality for subject identification, which has quality assurance applications in healthcare institutions.