Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification

Silverstein, Emanuel; Snyder, Michael

doi:10.1002/mp.12241

Cited by 21 publications

(30 citation statements)

References 11 publications

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“…Patient verification is a crucial means to ascertain the correctness of a patient’s identity. Biometric verification process for healthcare authenticates a patient’s identity in addition to the manual checking conducted by healthcare providers . Biometric identification usually refers to the closed‐set identification of someone whose biological fingerprint is stored in a database .…”

Section: Introductionmentioning

confidence: 99%

“…7 The term biometrics refers to the use of behavioral and biological characteristics to identify an individual. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] Biometrics are utilized as a part of either a verification or an identification system. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] Biometric verification refers to the authentication of a patient's identity by comparing a biological fingerprint with one prestored in a database.…”

Section: Introductionmentioning

confidence: 99%

“…Biometric verification process for healthcare authenticates a patient's identity in addition to the manual checking conducted by healthcare providers. [11][12][13][14][15] Biometric identification usually refers to the closed-set identification of someone whose biological fingerprint is stored in a database. [8][9][10] Patient identification is a crucial means to identify unconscious or unknown patients.…”

Section: Introductionmentioning

confidence: 99%

“…[25][26][27] We have previously reported on the usefulness of biological fingerprints using scout magnetic resonance (MR) images of the brain. 11 In other previous studies, medical image biometrics were reported using chest [19][20][21][22][25][26][27] and knee 23 X-ray images, optical surface images 13 , and patient X-ray images 24 acquired during a radiotherapy set-up process, facial images 15 extracted from computed tomography (CT), and facial images 12,14 acquired with mobile camera. Biometrics for healthcare preferred to be a simple process to enable identity verification under various patient conditions [1][2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

“…Biometrics for healthcare preferred to be a simple process to enable identity verification under various patient conditions [1][2][3][4][5] . Furthermore, although additional biometric input devices were used to acquire and process patient characteristics in some studies, [12][13][14] it is desirable to avoid such additional devices for appropriate handling of private information.…”

Section: Introductionmentioning

confidence: 99%

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Biological fingerprint using scout computed tomographic images for positive patient identification

2019

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biological fingerprint using scout computed tomographic images for positive patient identification

2019

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Accuracy of surface‐guided patient setup for conventional radiotherapy of brain and nasopharynx cancer

Lee

Huang

Zhang

et al. 2021

J Applied Clin Med Phys

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To evaluate the accuracy of surface-guided radiotherapy (SGRT) in cranial patient setup by direct comparison between optical surface imaging (OSI) and conebeam computed tomography (CBCT), before applying SGRT-only setup for conventional radiotherapy of brain and nasopharynx cancer. Methods and Materials: Using CBCT as reference, SGRT setup accuracy was examined based on 269 patients (415 treatments) treated with frameless cranial stereotactic radiosurgery (SRS) during 2018-2019. Patients were immobilized in customized head molds and open-face masks and monitored using OSI during treatment. The facial skin area in planning CT was used as OSI region of interest (ROI)for automatic surface alignment and the skull was used as the landmark for automatic CBCT/CT registration. A 6 degrees of freedom (6DOF) couch was used.Immediately after CBCT setup, an OSI verification image was captured, recording the SGRT setup differences. These differences were analyzed in 6DOFs and as a function of isocenter positions away from the anterior surface to assess OSI-ROI bias. The SGRT in-room setup time was estimated and compared with CBCT and orthogonal 2D kilovoltage (2DkV) setups. Results:The SGRT setup difference (magnitude) is found to be 1.0 AE 2.5 mm and 0.1˚AE1.4˚on average among 415 treatments and within 5 mm/3˚with greater than 95% confidence level (P < 0.001). Outliers were observed for very-posterior isocenters: 15 differences (3.6%) are >5.0mm and 9 (2.2%) are >3.0˚. The setup differences show minor correlations (|r| < 0.45) between translational and rotational DOFs and a minor increasing trend (<1.0 mm) in the anterior-to-posterior direction.The SGRT setup time is 0.8 AE 0.3 min, much shorter than CBCT (5 AE 2 min) and 2DkV (2 AE 1 min) setups. Conclusion:This study demonstrates that SGRT has sufficient accuracy for fast inroom patient setup and allows real-time motion monitoring for beam holding during treatment, potentially useful to guide radiotherapy of brain and nasopharynx cancer with standard fractionation.

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CT‐less electron radiotherapy simulation and planning with a consumer 3D camera

Skinner

Knopp

Wang

et al. 2021

J Applied Clin Med Phys

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Purpose: Electron radiation therapy dose distributions are affected by irregular body surface contours. This study investigates the feasibility of three-dimensional ( 3D) cameras to substitute for the treatment planning computerized tomography (CT) scan by capturing the body surfaces to be treated for accurate electron beam dosimetry.Methods: Dosimetry was compared for six electron beam treatments to the nose, toe, eye, and scalp using full CT scan, CT scan with Hounsfield Unit (HU) overridden to water (mimic 3D camera cases), and flat-phantom techniques. Radiation dose was prescribed to a depth on the central axis per physician's order, and the monitor units (MUs) were calculated. The 3D camera spatial accuracy was evaluated by comparing the 3D surface of a head phantom captured by a 3D camera and that generated with the CT scan in the treatment planning system. A clinical case is presented, and MUs were calculated using the 3D camera body contour with HU overridden to water.Results: Across six cases the average change in MUs between the full CT and the 3Dwater (CT scan with HU overridden to water) calculations was 1.3% with a standard deviation of 1.0%. The corresponding hotspots had a mean difference of 0.4% and a standard deviation of 1.9%. The 3D camera captured surface of a head phantom was found to have a 0.59 mm standard deviation from the surface derived from the CT scan. In-vivo dose measurements (213 ± 8 cGy) agreed with the 3D-camera planned dose of 209 ± 6 cGy, compared to 192 ± 6 cGy for the flat-phantom calculation (same MUs).Conclusions: Electron beam dosimetry is affected by irregular body surfaces. 3D cameras can capture irregular body contours which allow accurate dosimetry of electron beam treatment as an alternative to costly CT scans with no extra exposure to radiation. Tools and workflow for clinical implementation are provided.

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Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification

Cited by 21 publications

References 11 publications

Biological fingerprint using scout computed tomographic images for positive patient identification

Biological fingerprint using scout computed tomographic images for positive patient identification

Accuracy of surface‐guided patient setup for conventional radiotherapy of brain and nasopharynx cancer

CT‐less electron radiotherapy simulation and planning with a consumer 3D camera

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