title: Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: comparison with invasive catheterisation.
abstract: OBJECTIVE
  True automated detection of coronary artery stenoses might be useful whenever expert evaluation is not available, or as a "second reader" to enhance diagnostic confidence. We evaluated the accuracy of a PC-based stenosis detection tool alone and combined with expert interpretation.
  
  
  METHODS
  One hundred coronary CT angiography datasets were evaluated with the automated software alone, by manual interpretation (axial images, multiplanar reformations and maximum intensity projections in free double-oblique planes), and by expert interpretation aware of the automated findings. Stenoses ≥ 50 % were noted per-vessel and per-patient, and compared with invasive angiography.
  
  
  RESULTS
  Automated post-processing was successful in 90 % of patients (88 % of vessels). When excluding uninterpretable datasets, per-patient sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 89 %, 79 %, 74 % and 92 % (per-vessel: 82 %, 85 %, 48 % and 96 %). All 100 datasets were evaluable by expert interpretation. Per-patient sensitivity, specificity, PPV and NPV were 95 %, 95 %, 93 % and 97 % (per-vessel: 89 %,98 %, 88 % and 98 %). Knowing the results of automated interpretation did not improve the performance of expert readers.
  
  
  CONCLUSIONS
  Automated off-line post-processing of coronary CT angiography shows adequate sensitivity, but relatively low specificity in coronary stenosis detection. It does not increase accuracy of expert interpretation. Failure of post-processing in 10 % of all patients necessitates additional manual image work-up.
  
  
  CONCLUSIONS
  • Coronary CT angiography is increasingly used for detection of coronary artery stenosis • Computer assisted diagnosis might facilitate and speed up interpretation • Performance in properly segmented cases compared favourably with manual image interpretation • However, automated segmentation failed in about 10 % of cases • Manual reading is still mandatory; computer assisted diagnosis can provide a useful second read.
authors.affiliation: Department of Radiology, University of Erlangen, Maximiliansplatz 1, 91054 Erlangen, Germany. katharina.anders@uk-erlangen.de
authors.forename: Katharina
authors.initials: K
authors.lastname: Anders
authors.forename: Stephan
authors.initials: S
authors.lastname: Achenbach
authors.forename: Isabel
authors.initials: I
authors.lastname: Petit
authors.forename: Werner G
authors.initials: WG
authors.lastname: Daniel
authors.forename: Michael
authors.initials: M
authors.lastname: Uder
authors.forename: Tobias
authors.initials: T
authors.lastname: Pflederer
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D006328:Cardiac Catheterization / Q000379:methods*; D017023:Coronary Angiography / Q000379:methods*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation; D014057:Tomography, X-Ray Computed / Q000379:methods*