3 469

Cited 0 times in

Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain.

Authors
 Ki-Woon Kang  ;  Hyuk-Jae Chang  ;  Hackjoon Shim  ;  Young-Jin Kim  ;  Byoung-Wook Choi  ;  Woo-In Yang  ;  Jee-Young Shim  ;  Jongwon Ha  ;  Namsik Chung 
Citation
 EUROPEAN JOURNAL OF RADIOLOGY, Vol.81(4) : 640-646, 2012 
Journal Title
EUROPEAN JOURNAL OF RADIOLOGY
ISSN
 0720-048X 
Issue Date
2012
MeSH
Acute Disease ; Algorithms* ; Chest Pain/diagnostic imaging* ; Chest Pain/etiology ; Coronary Angiography/methods* ; Coronary Artery Disease/complications ; Coronary Artery Disease/diagnostic imaging* ; Feasibility Studies ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Automated/methods* ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Reproducibility of Results ; Sensitivity and Specificity ; Tomography, X-Ray Computed/methods*
Keywords
Automatic computer-assisted detection ; Coronary artery disease ; Acute chest pain
Abstract
Automatic computer-assisted detection (auto-CAD) of significant coronary artery disease (CAD) in coronary computed tomography angiography (cCTA) has been shown to have relatively high accuracy. However, to date, scarce data are available regarding the performance of auto-CAD in the setting of acute chest pain. This study sought to demonstrate the feasibility of an auto-CAD algorithm for cCTA in patients presenting with acute chest pain. We retrospectively investigated 398 consecutive patients (229 male, mean age 50±21 years) who had acute chest pain and underwent cCTA between Apr 2007 and Jan 2011 in the emergency department (ED). All cCTA data were analyzed using an auto-CAD algorithm for the detection of >50% CAD on cCTA. The accuracy of auto-CAD was compared with the formal radiology report. In 380 of 398 patients (18 were excluded due to failure of data processing), per-patient analysis of auto-CAD revealed the following: sensitivity 94%, specificity 63%, positive predictive value (PPV) 76%, and negative predictive value (NPV) 89%. After the exclusion of 37 cases that were interpreted as invalid by the auto-CAD algorithm, the NPV was further increased up to 97%, considering the false-negative cases in the formal radiology report, and was confirmed by subsequent invasive angiogram during the index visit. We successfully demonstrated the high accuracy of an auto-CAD algorithm, compared with the formal radiology report, for the detection of >50% CAD on cCTA in the setting of acute chest pain. The auto-CAD algorithm can be used to facilitate the decision-making process in the ED.
Full Text
http://www.sciencedirect.com/science/article/pii/S0720048X12000332
DOI
22304980
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Young Jin(김영진) ORCID logo https://orcid.org/0000-0002-6235-6550
Shim, Chi Young(심지영) ORCID logo https://orcid.org/0000-0002-6136-0136
Shim, Hack Joon(심학준)
Yang, Woo In(양우인)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Chung, Nam Sik(정남식)
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
Ha, Jong Won(하종원) ORCID logo https://orcid.org/0000-0002-8260-2958
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/90236
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links