Biological fingerprint using scout computed tomographic images for positive patient identification

Yasuyuki Ueda, Junji Morishita, Tadashi Hongyo

研究成果: ジャーナルへの寄稿記事

抄録

Purpose: Management 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. Methods: Scout 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). Results: A 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. Conclusions: Our 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.

元の言語英語
ページ(範囲)4600-4609
ページ数10
ジャーナルMedical physics
46
発行部数10
DOI
出版物ステータス出版済み - 10 1 2019

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Dermatoglyphics
Tomography
ROC Curve
Pelvis
Abdomen
Artifacts
Thorax
Metals
Area Under Curve
Point-of-Care Systems
Patient Safety
Health Personnel
Delivery of Health Care

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Radiology Nuclear Medicine and imaging

これを引用

Biological fingerprint using scout computed tomographic images for positive patient identification. / Ueda, Yasuyuki; Morishita, Junji; Hongyo, Tadashi.

:: Medical physics, 巻 46, 番号 10, 01.10.2019, p. 4600-4609.

研究成果: ジャーナルへの寄稿記事

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abstract = "Purpose: Management 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. Methods: Scout 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). Results: A 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. Conclusions: Our 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.",
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