[Development of an automated patient-recognition method for digital chest radiographs using edge-enhanced images]

New solutions for automated image recognition and identification: challenges to radiologic technology and forensic pathology

This paper outlines the history of biometrics for personal identification, the current status of the initial biological fingerprint techniques for digital chest radiography, and patient verification during medical imaging, such as computed tomography and magnetic resonance imaging. Automated image recognition and identification developed for clinical images without metadata could also be applied to the identification of victims in mass disasters or other unidentified individuals. The development of methods that are adaptive to a wide range of recent imaging modalities in the fields of radiologic technology, patient safety, forensic pathology, and forensic odontology is still in its early stages. However, its importance in practice will continue to increase in the future.

Clinical application of biological fingerprints extracted from averaged chest radiographs and template-matching technique for preventing left-right flipping mistakes in chest radiography

We aimed to evaluate the identification performance achieved using biological fingerprints extracted from averaged chest radiographs and template-matching techniques for the prevention of left-right flipping mistakes. We produced averaged chest radiographs for each sex by averaging 100 posteroanterior chest radiographs. Further, 400 and 566 chest radiographs were used in consistency and validation tests, respectively, and they were flipped horizontally to produce flipped chest radiographs under the assumption that the left-right flipping mistake occurred. The correlation values obtained with chest radiographs and those obtained with flipped chest radiographs were calculated. When we used correlation indices calculated from the correlation values from four biological fingerprints except for the lung apex, 96.5% (386/400) and 95.8% (542/566) of the left or right sides were identified correctly in the consistency and validation tests, respectively. This result indicates that our proposed method would be promising for the prevention of left-right flipping mistakes.

Development of a computer-assisted forensic radiographic identification method using the lateral cervical and lumbar spine

Medical examiners and coroners (ME/C) in the United States hold statutory responsibility to identify deceased individuals who fall under their jurisdiction. The computer-assisted decedent identification (CADI) project was designed to modify software used in diagnosis and treatment of spinal injuries into a mathematically validated tool for ME/C identification of fleshed decedents. CADI software analyzes the shapes of targeted vertebral bodies imaged in an array of standard radiographs and quantifies the likelihood that any two of the radiographs contain matching vertebral bodies. Six validation tests measured the repeatability, reliability, and sensitivity of the method, and the effects of age, sex, and number of radiographs in array composition. CADI returned a 92-100% success rate in identifying the true matching pair of vertebrae within arrays of five to 30 radiographs. Further development of CADI is expected to produce a novel identification method for use in ME/C offices that is reliable, timely, and cost-effective.

Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy

We have developed a computerized method for estimating patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy. The patient setup errors were estimated based on a template-matching technique that compared the portal image and a localized pelvic template image with a clinical target volume produced from a digitally reconstructed radiography (DRR) image of each patient. We evaluated the proposed method by calculating the residual error between the patient setup error obtained by the proposed method and the gold standard setup error determined by consensus between two radiation oncologists. Eleven training cases with prostate cancer were used for development of the proposed method, and then we applied the method to 10 test cases as a validation test. As a result, the residual errors in the anterior-posterior, superior-inferior and left-right directions were smaller than 2 mm for the validation test. The mean residual error was 2.65 ± 1.21 mm in the Euclidean distance for training cases, and 3.10 ± 1.49 mm for the validation test. There was no statistically significant difference in the residual error between the test for training cases and the validation test (P = 0.438). The proposed method appears to be robust for detecting patient setup error in the treatment of prostate cancer radiotherapy.

Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs

RATIONALE AND OBJECTIVE

The aim of the study was to survey misfiled cases in a picture archiving and communication system environment at two hospitals and to demonstrate the potential usefulness of an automated patient recognition method for posteroanterior chest radiographs based on a template-matching technique designed to prevent filing errors.

MATERIALS AND METHODS

We surveyed misfiled cases obtained from different modalities in one hospital for 25 months, and misfiled cases of chest radiographs in another hospital for 17 months. For investigating the usefulness of an automated patient recognition and identification method for chest radiographs, a prospective study has been completed in clinical settings at the latter hospital.

RESULTS

The total numbers of misfiled cases for different modalities in one hospital and for chest radiographs in another hospital were 327 and 22, respectively. The misfiled cases in the two hospitals were mainly the result of human errors (eg, incorrect manual entries of patient information, incorrect usage of identification cards in which an identification card for the previous patient was used for the next patient's image acquisition). The prospective study indicated the usefulness of the computerized method for discovering misfiled cases with a high performance (ie, an 86.4% correct warning rate for different patients and 1.5% incorrect warning rate for the same patients).

CONCLUSION

We confirmed the occurrence of misfiled cases in the two hospitals. The automated patient recognition and identification method for chest radiographs would be useful in preventing wrong images from being stored in the picture archiving and communication system environment.