Biological fingerprint using scout computed tomographic images for positive patient identification
Biological fingerprint using scout computed tomographic images for positive patient identification
复制标题
使用侦察计算机断层扫描图像进行生物指纹以进行积极的患者识别
DOI：
10.1002/mp.13779
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发表时间：
2019
期刊：
影响因子：
3.8
通讯作者：
Tadashi Hongyo
中科院分区：
文献类型：
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作者：
Yasuyuki Ueda;Junji Morishita;Tadashi Hongyo
在使用现代医疗系统时，患者身份识别管理是确保患者安全的一个重要问题。患者识别错误主要可归因于人为错误或系统问题。容错系统，如生物识别系统，应该能够防止或减轻潜在的错误识别发生。在这里，我们提出了使用侦察计算机断层扫描（CT）图像的生物识别患者身份验证，并提出了使用这种技术在临床setting.MethodsScout CT图像的定量准确性结果从常规检查的胸部，腹部和骨盆被用作生物指纹。我们通过使用局部特征提取和匹配算法比较图像特征的估计值，评价了随访与基线图像的相似性。根据受试者工作特征（ROC）曲线、ROC曲线下面积（AUC）和等错误率（EER）评估验证性能。根据累积匹配特征曲线和秩一识别率（R1）评价闭集识别性能。结果共对619例（男383例，女236例，年龄21-92岁）在同一CT系统上进行基线和随访胸腹盆CT扫描的患者进行验证和闭集识别。在考虑的评价范围内，AUC、EER和R1的最高性能分别为0.998、1.22%和99.7%。此外，为了确定在存在金属伪影的情况下性能是否降低，将患者分为两组，即具有（255名患者）和不具有（364名患者）金属伪影的侦察图像，并且使用非配对Delong检验对两个ROC曲线进行显著性检验。当使用足够数量的局部特征时，在存在和不存在金属伪影的情况下，ROC性能之间没有显着差异。我们提出的技术表明，性能与传统的生物识别方法相比，当使用胸部，腹部和骨盆侦察CT图像。因此，该方法具有使用可用的胸部、腹部和骨盆侦察CT图像来发现不充分的患者信息的潜力;此外，本发明还它可以广泛应用于常规成人CT扫描，其中没有由于疾病或衰老而存在的显着身体结构影响。结论我们提出的方法可以在护理点获得准确的患者信息，并帮助医疗保健提供者验证患者的身份是否准确匹配。我们相信该方法是解决患者错误识别问题的关键。
PurposeManagement of patient identification is an important issue that should be addressed to ensure patient safety while using modern healthcare systems. Patient identification errors can be mainly attributed to human errors or system problems. An error‐tolerant system, such as a biometric system, should be able to prevent or mitigate potential misidentification occurrences. Herein, we propose the use of scout computed tomography (CT) images for biometric patient identity verification and present the quantitative accuracy outcomes of using this technique in a clinical setting.MethodsScout CT images acquired from routine examinations of the chest, abdomen, and pelvis were used as biological fingerprints. We evaluated the resemblance of the follow‐up with the baseline image by comparing the estimates of the image characteristics using local feature extraction and matching algorithms. The verification performance was evaluated according to the receiver operating characteristic (ROC) curves, area under the ROC curves (AUC), and equal error rates (EER). The closed‐set identification performance was evaluated according to the cumulative match characteristic curves and rank‐one identification rates (R1).ResultsA total of 619 (383 males, 236 females, age range 21–92 years) patients who underwent baseline and follow‐up chest–abdomen–pelvis CT scans on the same CT system were analyzed for verification and closed‐set identification. The highest performances of AUC, EER, and R1 were 0.998, 1.22%, and 99.7%, respectively, in the considered evaluation range. Furthermore, to determine whether the performance decreased in the presence of metal artifacts, the patients were classified into two groups, namely scout images with (255 patients) and without (364 patients) metal artifacts, and the significance test was performed for two ROC curves using the unpaired Delong's test. No significant differences were found between the ROC performances in the presence and absence of metal artifacts when using a sufficient number of local features. Our proposed technique demonstrated that the performance was comparable to that of conventional biometrics methods when using chest, abdomen, and pelvis scout CT images. Thus, this method has the potential to discover inadequate patient information using the available chest, abdomen, and pelvis scout CT image; moreover, it can be applied widely to routine adult CT scans where no significant body structure effects due to illness or aging are present.ConclusionsOur proposed method can obtain accurate patient information available at the point‐of‐care and help healthcare providers verify whether a patient’s identity is matched accurately. We believe the method to be a key solution for patient misidentification problems.