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Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study

Authors
 Ji Hoon Kim  ;  Sang Gil Han  ;  Ara Cho  ;  Hye Jung Shin  ;  Song-Ee Ba다 
Citation
 BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol.21(1) : 311, 2021-11 
Journal Title
BMC MEDICAL INFORMATICS AND DECISION MAKING
Issue Date
2021-11
MeSH
Deep Learning* ; Emergency Service, Hospital ; Humans ; Physicians* ; Radiography, Thoracic ; Self-Help Devices*
Keywords
Chest radiograph ; Decision-making ; Deep learning-based assistive technology ; Emergency department
Abstract
Background: Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians.

Methods: We conducted a prospective interventional study using a web-based performance assessment system. Study participants were recruited through the official notice targeting board for certified emergency physicians and residents working at the present ED. Of the eight ED physicians who volunteered to participate in the study, seven ED physicians were included, while one participant declared withdrawal during performance assessment. Seven physicians' CR interpretations and clinical decision-making were assessed based on the clinical data from 388 patients, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. ED physicians with < 24 months of experience were defined as 'inexperienced'.

Results: Among the 388 simulated cases, 259 (66.8%) had CR abnormality. Their median value of abnormality score measured by DLCR was 59.3 (31.77, 76.25) compared to a score of 3.35 (1.57, 8.89) for cases of normal CR. There was a difference in performance between ED physicians working with and without DLCR (AUROC: 0.801, P < 0.001). The diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% confidence interval [CI] 0.884-0.920); concurrently, the kappa value for the experienced group was 0.956 (95% CI 0.934-0.979), and that for the inexperienced group was 0.862 (95% CI 0.835-0.889).

Conclusions: This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced the clinical decision-making of inexperienced physicians more strongly than that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice.
Files in This Item:
T202105444.pdf Download
DOI
10.1186/s12911-021-01679-4
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ji Hoon(김지훈) ORCID logo https://orcid.org/0000-0002-0070-9568
Baek, Song Ee(백송이) ORCID logo https://orcid.org/0000-0001-8146-2570
Cho, Ara(조아라)
Han, Sang Gil(한상길)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187286
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