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Updated: Jan 15, 2026

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

Alistair Yap1, Kyongtae T Bae1

  • 1Department of Diagnostic Radiology, The University of Hong Kong, Queen Mary Hospital, Pokfulam Road, Hong Kong Special Administrative Region.

Computers in Biology and Medicine
|October 16, 2025
PubMed
Summary

Radiologists can now automatically identify patients from X-rays using deep learning. This system improves patient safety by preventing misidentification errors in radiology follow-up examinations, enhancing healthcare quality assurance.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biometrics

Background:

  • Accurate patient identification is crucial for radiology follow-up examinations.
  • Manual verification is challenging due to patient condition changes and imaging variations.
  • Misidentification errors can lead to significant patient safety risks.

Purpose of the Study:

  • To develop an automated system for patient identification from radiographs using deep learning.
  • To enhance the quality assurance of radiology reporting.
  • To explore the potential of radiographs as a biometric modality.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for automatic patient identification.
  • Employed deep metric learning to train models for matching radiographs.
Keywords:
BiometricsChest radiographDeep learningQuality assuranceSkeletal radiograph

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  • Trained models for various body parts (chest, knees, pelvis, hands) and multi-view chest images.

    • Main Results:

    • Achieved over 0.98 true positive rate (TPR) at 0.001 false positive rate (FPR) for most models.
    • Attained over 0.96 rank-1 TPR on internal test datasets.
    • The multi-view chest radiograph model demonstrated high accuracy in matching frontal and lateral views.

    Conclusions:

    • The developed CNN system effectively identifies patients from radiographs.
    • Radiographs show potential as a reliable biometric modality for subject identification.
    • This technology offers significant quality assurance benefits for healthcare institutions.