Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study.
We aimed to evaluate the ability of deep learning (DL) models to identify patients from a paired chest radiograph (CXR) and compare their performance with that of human experts. In this retrospective study, patient identification DL models were developed using 240,004 CXRs. The models were validated...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 694 - 703 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
|
| 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=184081747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081747 184081747 184081747 10.1007/s10278-024-01245-0 184081747 ppf: 694 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study. aug: au: Kim, Kiduk Cho, Kyungjin Eo, Yujeong Kim, Jeeyoung Yun, Jihye Ahn, Yura Seo, Joon Beom Hong, Gil-Sun Kim, Namkug affil: https://ror.org/03s5q0090 Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil, Songpa-Gu, 05505, Seoul, Republic of Korea sug: subj: Deep Learning Radiography, Thoracic Convolutional Neural Networks Patient Identification Artificial Intelligence Diagnostic Errors Image Interpretation, Computer Assisted Algorithms Human Male Female Funding Source Medical Informatics Retrospective Design Descriptive Statistics Comparative Studies Diagnostic Errors Prevention and Control Radiologists Male Female ab: We aimed to evaluate the ability of deep learning (DL) models to identify patients from a paired chest radiograph (CXR) and compare their performance with that of human experts. In this retrospective study, patient identification DL models were developed using 240,004 CXRs. The models were validated using multiple datasets, namely, internal validation, CheXpert, and Chest ImaGenome (CIG), which include different populations. Model performance was analyzed in terms of disease change status. The performance of the models to identify patients from paired CXRs was compared with three junior radiology residents (group I), two senior radiology residents (group II), and two board-certified expert radiologists (group III). For the reader study, 240 patients (age, 56.617 ± 13.690 years, 113 females, 160 same pairs) were evaluated. A one-sided non-inferiority test was performed with a one-sided margin of 0.05. SimChest, our similarity-based DL model, demonstrated the best patient identification performance across multiple datasets, regardless of disease change status (internal validation [area under the receiver operating characteristic curve range: 0.992–0.999], CheXpert [0.933–0.948], and CIG [0.949–0.951]). The radiologists identified patients from the paired CXRs with a mean accuracy of 0.900 (95% confidence interval: 0.852–0.948), with performance increasing with experience (mean accuracy:group I [0.874], group II [0.904], group III [0.935], and SimChest [0.904]). SimChest achieved non-inferior performance compared to the radiologists (P for non-inferiority: 0.015). The findings of this diagnostic study indicate that DL models can screen for patient misidentification using a pair of CXRs non-inferiorly to human experts. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|