RATIONALE AND OBJECTIVES: The purpose of this study was to demonstrate the potential usefulness of "biological fingerprints" in chest radiographs for automated patient recognition and identification. MATERIALS AND METHODS: Thoracic fields, cardiac shadows, the superior mediastinum, lung apices, a part of the right lung, and the right lower lung that includes the costophrenic angle were used as biological fingerprints in chest radiographs. Each of the biological fingerprints in a current chest radiograph was used as a template for determination of the correlation value with the corresponding biological fingerprint in a previous chest radiograph for patient recognition and identification. The overall performance of the method developed was examined in terms of receiver operating characteristic curves. RESULTS: Receiver operating characteristic curves obtained with different biological fingerprints, except for the part of the right lung, indicated a high performance in identifying patients. These results showed that a new concept of biological fingerprints in radiologic images would be useful in patient recognition and identification. The low performance with the part of the right lung seems to be related to a general observation that this region does not usually include features unique to a specific patient. The performance of the artificial neural networks by use of a combination of five biological fingerprints was higher than results obtained with each biological fingerprint. CONCLUSION: The use of automated patient identification based on biological fingerprints in chest radiographs is promising for helping to discover misfiled patient images, especially in a picture archiving and communication system environment.
RATIONALE AND OBJECTIVES: The purpose of this study was to demonstrate the potential usefulness of "biological fingerprints" in chest radiographs for automated patient recognition and identification. MATERIALS AND METHODS: Thoracic fields, cardiac shadows, the superior mediastinum, lung apices, a part of the right lung, and the right lower lung that includes the costophrenic angle were used as biological fingerprints in chest radiographs. Each of the biological fingerprints in a current chest radiograph was used as a template for determination of the correlation value with the corresponding biological fingerprint in a previous chest radiograph for patient recognition and identification. The overall performance of the method developed was examined in terms of receiver operating characteristic curves. RESULTS: Receiver operating characteristic curves obtained with different biological fingerprints, except for the part of the right lung, indicated a high performance in identifying patients. These results showed that a new concept of biological fingerprints in radiologic images would be useful in patient recognition and identification. The low performance with the part of the right lung seems to be related to a general observation that this region does not usually include features unique to a specific patient. The performance of the artificial neural networks by use of a combination of five biological fingerprints was higher than results obtained with each biological fingerprint. CONCLUSION: The use of automated patient identification based on biological fingerprints in chest radiographs is promising for helping to discover misfiled patient images, especially in a picture archiving and communication system environment.