Usefulness of Biological Fingerprints and Template Matching Techniques in Bedside Chest Radiography for Patient Identification and Preventing Filing Mistakes
| Autor: | Keita Takahashi, Toyoyuki Kato, Yuki Sakai, Akiko Hattori, K Iwase, Yoichiro Shimizu |
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| Rok vydání: | 2018 |
| Předmět: |
Male
Patient Identification Systems medicine.medical_specialty Receiver operating characteristic business.industry Point-of-Care Systems Radiography Template matching Filing General Medicine Patient identification Radiology Information Systems Picture archiving and communication system ROC Curve Female patient medicine Humans Female Radiography Thoracic Radiology business Correlation index Area under the roc curve |
| Zdroj: | Japanese Journal of Radiological Technology. 74:1154-1162 |
| ISSN: | 1881-4883 0369-4305 |
| DOI: | 10.6009/jjrt.2018_jsrt_74.10.1154 |
| Popis: | The purpose of this study was to investigate whether patients can be identified by using biological fingerprints extracted from bedside chest radiographs and template matching techniques for preventing filing mistakes in a picture archiving and communication system (PACS) server. A total of 400 bedside chest radiographs from 100 male and 100 female patients with current and previous images were used for evaluating patient identification performance. Five biological fingerprints were extracted from 200 previous images using the averaged bedside chest radiographs, produced for each sex and detector size. The correlation values of 200 same patients and 39,800 different patients were calculated as a similarity index, and used for the receiver operating characteristic (ROC) analysis. The patient identification performance was examined by using the correlation index calculated by the summation of correlation values obtained from five biological fingerprints. The sensitivity at 90.0% specificity was calculated using the correlation index. The correlation index for same patients was higher than that for different patients. The area under the ROC curve was 0.974. The patient identification performance was 76.0% (152/200), and the sensitivity at 90.0% specificity was 93.4% (37168/39800). Our results suggest that the proposed method may potentially be useful for preventing filing mistakes in bedside chest radiographs on a PACS server. |
| Databáze: | OpenAIRE |
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