Literature DB >> 25117643

Computer-aided CT coronary artery stenosis detection: comparison with human reading and quantitative coronary angiography.

Matthias Rief1, Anisha Kranz, Lisa Hartmann, Robert Roehle, Michael Laule, Marc Dewey.   

Abstract

To evaluate computer-aided stenosis detection for computed tomography coronary angiography (CTA) in comparison with human reading and conventional coronary angiography (CCA) as the reference standard. 50 patients underwent CTA and CCA and out of these 44 were evaluable for computer-aided stenosis detection. The diagnostic performance of the software and of human reading were compared and quantitative coronary angiography (QCA) served as the reference standard for the detection of significant stenosis (>50 %). Overall, three readers with high (reader 1), intermediate (reader 2) and low (reader 3) experience in cardiac CT imaging performed the manual CTA evaluation on a commercially available workstation, whereas the automated software processed the datasets without any human interaction. The prevalence of coronary artery disease was 41 % (18/44) and QCA indicated significant stenosis (>50 %) in 33 coronary vessels. The automated software accurately diagnosed 18 individuals with significant coronary artery disease (CAD), and correctly ruled out CAD in 10 patients. In summary the sensitivity of computer-aided detection was 100 %/94 % (per-patient/per-vessel) and the specificity was 38 %/70 %, the positive predictive value (PPV) was 53 %/42 % and the negative predictive value (NPV) was 100 %/98 %. In comparison, reader 1-3 showed per-patient sensitivities of 100/94/89 %, specificities of 73/69/50 %, PPVs of 72/68/55 % and NPVs of 100/95/87 %. Computer-aided detection yields a high NPV that is comparable to more experienced human readers. However, PPV is rather low and in the range of an unexperienced reader.

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Year:  2014        PMID: 25117643     DOI: 10.1007/s10554-014-0513-x

Source DB:  PubMed          Journal:  Int J Cardiovasc Imaging        ISSN: 1569-5794            Impact factor:   2.357


  23 in total

1.  Multislice CT coronary angiography: evaluation of an automatic vessel detection tool.

Authors:  M Dewey; D Schnapauff; M Laule; A Lembcke; A C Borges; W Rutsch; B Hamm; P Rogalla
Journal:  Rofo       Date:  2004-04

Review 2.  Computer-aided simple triage.

Authors:  Roman Goldenberg; Nathan Peled
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-04-16       Impact factor: 2.924

3.  Efficacy of computer aided analysis in detection of significant coronary artery stenosis in cardiac using dual source computed tomography.

Authors:  Anja J Reimann; Ilias Tsiflikas; Harald Brodoefel; Michael Scheuering; Daniel Rinck; Andreas F Kopp; Claus D Claussen; Martin Heuschmid
Journal:  Int J Cardiovasc Imaging       Date:  2008-09-28       Impact factor: 2.357

4.  In vivo evaluation of stent patency by 64-slice multidetector CT coronary angiography: shall we do it or not?

Authors:  Jiayin Zhang; Minghua Li; Zhigang Lu; Jingyu Hang; Jingwei Pan; Leiqing Sun
Journal:  Int J Cardiovasc Imaging       Date:  2011-04-03       Impact factor: 2.357

5.  Influence of observer experience and training on proficiency in coronary CT angiography interpretation.

Authors:  Christopher Herzog; J Matthias Kerl; Salvatore De Rosa; Tuna Tekin; Eike Boehme; Sven Liem; Miriam Scheuchenzuber; He-Ri Kim; Ralf W Bauer; Justin R Silverman; Peter L Zwerner; Hanns Ackermann; Thomas J Vogl; U Joseph Schoepf
Journal:  Eur J Radiol       Date:  2013-04-17       Impact factor: 3.528

6.  Prognostic value of coronary CT angiography on long-term follow-up of 6.9 years.

Authors:  Svetlana Dougoud; Tobias A Fuchs; Julia Stehli; Olivier F Clerc; Ronny R Buechel; Bernhard A Herzog; Sebastian Leschka; Hatem Alkadhi; Philipp A Kaufmann; Oliver Gaemperli
Journal:  Int J Cardiovasc Imaging       Date:  2014-04-08       Impact factor: 2.357

7.  CT coronary angiography using 16 and 64 simultaneous detector rows: intraindividual comparison.

Authors:  M Dewey; H Hoffmann; B Hamm
Journal:  Rofo       Date:  2007-05-09

8.  Abolition of respiratory-motion artifact in computed tomography coronary angiography with ultrafast examinations: a comparison between 64-row and 320-row multidetector scanners.

Authors:  Felipe S Torres; Andrew M Crean; Elsie T Nguyen; Ravi Menezes; Deirdre Doyle; Anoop P Ayyappan; Sobhi Abadi; Narinder Paul
Journal:  Can Assoc Radiol J       Date:  2009-10-09       Impact factor: 2.248

9.  Diagnostic accuracy of 320-row multidetector computed tomography coronary angiography in the non-invasive evaluation of significant coronary artery disease.

Authors:  Fleur R de Graaf; Joanne D Schuijf; Joëlla E van Velzen; Lucia J Kroft; Albert de Roos; Johannes H C Reiber; Eric Boersma; Martin J Schalij; Fabrizio Spanó; J Wouter Jukema; Ernst E van der Wall; Jeroen J Bax
Journal:  Eur Heart J       Date:  2010-01-04       Impact factor: 29.983

10.  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

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  6 in total

Review 1.  Cardiovascular imaging 2014 in the International Journal of Cardiovascular Imaging.

Authors: 
Journal:  Int J Cardiovasc Imaging       Date:  2015-03       Impact factor: 2.357

Review 2.  Plaque assessment by coronary CT.

Authors:  Bálint Szilveszter; Csilla Celeng; Pál Maurovich-Horvat
Journal:  Int J Cardiovasc Imaging       Date:  2015-08-18       Impact factor: 2.357

3.  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

4.  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

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

6.  Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video.

Authors:  Hiroto Yabushita; Shinichi Goto; Sunao Nakamura; Hideki Oka; Masamitsu Nakayama; Shinya Goto
Journal:  J Atheroscler Thromb       Date:  2020-10-02       Impact factor: 4.928

  6 in total

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