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Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications
Matthew S Macpherson1, Charles E Hutchinson1, Carolyn Horst1
1From the Mathematics Institute (M.S.M.), Warwick Medical School (C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group (G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom; Department of Radiology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom (C.H., V.G.); Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute, London, United Kingdom (G.M.).
Purpose:
To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets.
Materials And Methods:
This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction.
Results:
Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error.
Conclusion:
The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.
