Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations.
Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such methods have not been incorporated into clinical use, and their performance can degrade with variab...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 1941 - 1954 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950859 164241487 171950859 171950859 10.1007/s10278-023-00850-9 171950859 ppf: 1941 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations. aug: au: Ueda, Yasuyuki Morishita, Junji affil: https://ror.org/035t8zc32 Department of Medical Physics and Engineering, Area of Medical Imaging Technology and Science, Graduate School of Medicine, Division of Health Sciences, Osaka University, Osaka, Japan sug: subj: Patient Identification Methods Deep Learning Utilization Human Error Prevention and Control After Care Radiography, Thoracic Human Male Female Infant, Newborn Infant Child, Preschool Child Adolescence Adult Middle Age Aged Aged, 80 and Over Funding Source Neural Networks (Computer) Health Screening Hospitalization Descriptive Statistics Sensitivity and Specificity Malpractice Prevention and Control Automation Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such methods have not been incorporated into clinical use, and their performance can degrade with variability in the clinical images. Deep learning can be used to improve the performance of these methods. A novel method is proposed to automatically identify individuals among examined patients using posteroanterior (PA) and anteroposterior (AP) chest X-ray images. The proposed method uses deep metric learning based on a deep convolutional neural network (DCNN) to overcome the extreme classification requirements for patient validation and identification. It was trained on the NIH chest X-ray dataset (ChestX-ray8) in three steps: preprocessing, DCNN feature extraction with an EfficientNetV2-S backbone, and classification with deep metric learning. The proposed method was evaluated using two public datasets and two clinical chest X-ray image datasets containing data from patients undergoing screening and hospital care. A 1280-dimensional feature extractor pretrained for 300 epochs performed the best with an area under the receiver operating characteristic curve of 0.9894, an equal error rate of 0.0269, and a top-1 accuracy of 0.839 on the PadChest dataset containing both PA and AP view positions. The findings of this study provide considerable insights into the development of automated patient identification to reduce the possibility of medical malpractice due to human errors. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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