Literature DB >> 23735619

Diagnostic performance evaluation of a computer-aided simple triage system for coronary CT angiography in patients with intermediate risk for acute coronary syndrome.

Mathias Meyer1, U Joseph Schoepf, Christian Fink, Roman Goldenberg, Paul Apfaltrer, Joachim Gruettner, Diana Vajcs, Stefan O Schoenberg, Thomas Henzler.   

Abstract

RATIONALE AND
OBJECTIVES: Given the significance of coronary artery disease as the most important socioeconomic health care problem in the Western World, the application of computer-aided simple triage (CAST) systems to this disease would be desirable.
MATERIALS AND METHODS: In total, 93 patients with acute chest pain and an intermediate risk score for acute coronary syndrome underwent coronary computed tomography angiography (cCTA). Among those, 74 were of adequate image quality for automated analysis by a commercially available CAST system (COR Analyzer, RCADIA, Haifa, Israel). CAST findings were compared to human expert interpretation for the detection of significant stenosis (≥50%) in the left main, left anterior descending, circumflex, right coronary artery, or arterial branches. Further, one inexperienced observer evaluated all studies for significant stenoses alone and after 1 month guided by a CAST system as an initial read.
RESULTS: Human expert interpretation identified 37/74 patients with stenosis ≥50%, whereas the CAST detected 45 patients. The CAST system demonstrated a sensitivity of 100%/79% and a specificity of 78%/89% on a per-patient/per-vessel level, respectively. With CAST, the inexperienced readers' per-vessel sensitivity and positive predictive values significantly improved (P = .011, P = .009) from 69% and 41% to 91% and 74%, respectively.
CONCLUSIONS: The investigated CAST system for automatic stenosis detection can accurately identify patients with coronary artery stenosis ≥50% and may be of use as initial interpretation and triage of cCTA studies as well as a second reader for inexperienced readers, in absence of expert readers.
Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Coronary CT angiography; chest pain patient triage; computed-aided detection; computer-aided simple triage; coronary artery stenosis

Mesh:

Year:  2013        PMID: 23735619     DOI: 10.1016/j.acra.2013.02.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  5 in total

1.  Computer-aided stenosis detection at coronary CT angiography: effect on performance of readers with different experience levels.

Authors:  Christian Thilo; Mulugeta Gebregziabher; Felix G Meinel; Roman Goldenberg; John W Nance; Elisabeth M Arnoldi; Lashonda D Soma; Ullrich Ebersberger; Philip Blanke; Richard L Coursey; Michael A Rosenblum; Peter L Zwerner; U Joseph Schoepf
Journal:  Eur Radiol       Date:  2014-10-15       Impact factor: 5.315

2.  Computer-aided analysis of 64- and 320-slice coronary computed tomography angiography: a comparison with expert human interpretation.

Authors:  Moshrik Abd Alamir; Pamela Noack; Kristine H Jang; Jhanna A Moore; Roman Goldberg; Michael Poon
Journal:  Int J Cardiovasc Imaging       Date:  2018-04-25       Impact factor: 2.357

3.  Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.

Authors:  Jie Xu; Linli Chen; Xiaojia Wu; Chuanming Li; Guangyong Ai; Yuexi Liu; Bitong Tian; Dajing Guo; Zheng Fang
Journal:  Eur Radiol       Date:  2021-09-26       Impact factor: 5.315

4.  Evaluation of novice reader diagnostic performance in coronary CT angiography using an advanced cardiac software package.

Authors:  Peter Dankerl; Matthias Hammon; Alexey Tsymbal; Alexander Cavallaro; Stephan Achenbach; Michael Uder; Rolf Janka
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-11-08       Impact factor: 2.924

5.  Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors.

Authors:  Domenico De Santis; Giuseppe Tremamunno; Carlotta Rucci; Tiziano Polidori; Marta Zerunian; Giulia Piccinni; Luca Pugliese; Benedetta Masci; Nicolò Ubaldi; Andrea Laghi; Damiano Caruso
Journal:  Diagnostics (Basel)       Date:  2022-08-16
  5 in total

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