Usefulness of biological fingerprint in magnetic resonance imaging for patient verification

Abstract The purpose of our study is to investigate the feasibility of automated patient verification using multi-planar reconstruction (MPR) images generated from three-dimensional magnetic resonance (MR) imaging of the brain. Several anatomy-related MPR images generated from three-dimensional fast scout scan of each MR examination were used as biological fingerprint images in this study. The database of this study consisted of 730 temporal pairs of MR examination of the brain. We calculated the correlation value between current and prior biological fingerprint images of the same patient and also all combinations of two images for different patients to evaluate the effectiveness of our method for patient verification. The best performance of our system were as follows: a half-total error rate of 1.59 % with a false acceptance rate of 0.023 % and a false rejection rate of 3.15 %, an equal error rate of 1.37 %, and a rank-one identification rate of 98.6 %. Our method makes it possible to verify the identity of the patient using only some existing medical images without the addition of incidental equipment. Also, our method will contribute to patient misidentification error management caused by human errors.

[1]  L. Schulmeister,et al.  Patient misidentification in oncology care. , 2008, Clinical journal of oncology nursing.

[2]  P. Charache,et al.  Registration-associated patient misidentification in an academic medical center: causes and corrections. , 2007, Joint Commission journal on quality and patient safety.

[3]  Joseph A. O'Sullivan,et al.  ECG Biometric Recognition: A Comparative Analysis , 2012, IEEE Transactions on Information Forensics and Security.

[4]  P. Pagliaro,et al.  Patients' positive identification systems. , 2009, Blood transfusion = Trasfusione del sangue.

[5]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[6]  Lior Shamir,et al.  Biometric identification using knee X-rays , 2009, Int. J. Biom..

[7]  Yasuyuki Ueda,et al.  SU-E-I-72: Biological Fingerprint for Automatic Patient Identification and Verification by Use of Three-Dimensional Magnetic Resonance Imaging with Multi-Planar Reconstruction Scout Images , 2015 .

[8]  Ting Chen,et al.  Automatic Alignment of Brain MR Scout Scans Using Data-adaptive Multi-structural Model , 2011, MICCAI.

[9]  Neil Yager,et al.  An Introduction to Biometric Data Analysis , 2009 .

[10]  Gert Pfurtscheller,et al.  Brainwave Biometrics:A New Feature Extraction Approach with the Cepstral Analysis Method , 2012 .

[11]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[12]  Abhishek Vaish,et al.  Brainwave based user identification system: A pilot study in robotics environment , 2015, Robotics Auton. Syst..

[13]  Anders M. Dale,et al.  On-line automatic slice positioning for brain MR imaging , 2005, NeuroImage.

[14]  Alexis B. Carter,et al.  Patient misidentifications caused by errors in standard bar code technology. , 2010, Clinical chemistry.

[15]  K. Doi,et al.  Computerized image-searching method for finding correct patients for misfiled chest radiographs in a PACS server by use of biological fingerprints , 2013, Radiological Physics and Technology.

[16]  Farzad Towhidkhah,et al.  Performance enhancement for audio-visual speaker identification using dynamic facial muscle model , 2006, Medical and Biological Engineering and Computing.

[17]  S Katsuragawa,et al.  An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment. , 2001, Medical physics.

[18]  Penny Holmes,et al.  Is it possible to eliminate patient identification errors in medical imaging? , 2011, Journal of the American College of Radiology : JACR.

[19]  Donald L Fisher,et al.  Patient identification errors are common in a simulated setting. , 2010, Annals of emergency medicine.

[20]  Kunio Doi,et al.  Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs. , 2005, Academic radiology.

[21]  P. Maurette [To err is human: building a safer health system]. , 2002, Annales francaises d'anesthesie et de reanimation.

[22]  M. G. Bissell Patient Misidentification in Laboratory Medicine: A Qualitative Analysis of 227 Root Cause Analysis Reports in the Veterans Health Administration , 2011 .

[23]  Lior Shamir,et al.  MRI-based knee image for personal identification , 2013, Int. J. Biom..

[24]  F. Bennardello,et al.  Use of an identification system based on biometric data for patients requiring transfusions guarantees transfusion safety and traceability. , 2009, Blood transfusion = Trasfusione del sangue.

[25]  Kunio Doi,et al.  Potential usefulness of biological fingerprints in chest radiographs for automated patient recognition and identification. , 2004, Academic radiology.

[26]  Lynette I. Millett,et al.  Biometric Recognition: Challenges and Opportunities , 2010 .