title: The use of computer-aided detection for the assessment of pulmonary arterial filling defects at computed tomographic angiography.
abstract: OBJECTIVE
  To validate a computer-aided detection (CAD) tool for the detection of pulmonary arterial filling defects at computed tomographic pulmonary angiography (CTPA) and to assess its benefit for readers of different levels of experience.
  
  
  METHODS
  One hundred consecutive CTPA studies were retrospectively evaluated by a chest radiologist for presence of emboli, serving as the reference standard. Subsequently, examinations were analyzed using commercially available second-generation CAD software (ImageChecker CT, version 2.1; R2 Technology, Inc., Sunnyvale, Calif). The staff radiologist assessed all CAD marks and classified them as true positive or false positive (FP), and any unmarked emboli were classified as false negative. Computer-aided detection software was also evaluated on a case basis compared with the reference standard.For the second part of the study, the 100 CTPAs were reviewed by 3 additional readers of different levels of experience, both without and with CAD, and findings correlated with the reference standard.
  
  
  RESULTS
  Twenty-one studies (21%) were positive for pulmonary embolism. Of these, 18 were true positive on a case basis, and 3 were false negative. Of the 79 negative studies, 16 were true negative with no CAD marks, and the remaining 63 were FP. On a case basis, CAD sensitivity was 86%, specificity was 20%, negative predictive value was 84%, and positive predictive value (PPV) was 22%.Overall, the CAD software yielded 318 marks, identifying 64 of 93 emboli with an additional 254 FP marks. On a mark basis, sensitivity was 69%, and PPV was 20%.Computer-aided detection did not influence the most experienced reader (a chest fellow). Although CAD improved the subjective confidence of the second-year resident in some cases, it had no influence on overall interpretation or accuracy. Computer-aided detection improved accuracy only for the most inexperienced reader, helping this reader to identify 9 emboli not initially appreciated.
  
  
  CONCLUSIONS
  Computer-aided detection specificity and PPV are poor due to expected FP marks, although, often, these can be easily dismissed. However, CAD software may play an important role as a second reader for residents or inexperienced readers.
authors.affiliation: Department of Medical Imaging, University Health Network, Toronto, ON, Canada.
authors.forename: Anna C
authors.initials: AC
authors.lastname: Walsham
authors.forename: Heidi C
authors.initials: HC
authors.lastname: Roberts
authors.forename: Hany Mehdizadeh
authors.initials: HM
authors.lastname: Kashani
authors.forename: Christopher N
authors.initials: CN
authors.lastname: Mongiardi
authors.forename: Yuen-Li
authors.initials: YL
authors.lastname: Ng
authors.forename: Demetris A
authors.initials: DA
authors.lastname: Patsios
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011651:Pulmonary Artery / Q000000981:diagnostic imaging*; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*