title: Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?
abstract: OBJECTIVE
  To evaluate the effect of a computer-aided detection (CAD) algorithm on the performance of novice readers for detection of pulmonary embolism (PE) at CT pulmonary angiography (CTPA).
  
  
  METHODS
  We included CTPA examinations of 79 patients (50 female, 52 ± 18 years). Studies were evaluated by two independent inexperienced readers who marked all vessels containing PE. After 3 months all studies were reevaluated by the same two readers, this time aided by CAD prototype. A consensus read by three expert radiologists served as the reference standard. Statistical analysis used χ(2) and McNemar testing.
  
  
  RESULTS
  Expert consensus revealed 119 PEs in 32 studies. For PE detection, the sensitivity of CAD alone was 78%. Inexperienced readers' initial interpretations had an average per-PE sensitivity of 50%, which improved to 71% (p < 0.001) with CAD as a second reader. False positives increased from 0.18 to 0.25 per study (p = 0.03). Per-study, the readers initially detected 27/32 positive studies (84%); with CAD this number increased to 29.5 studies (92%; p = 0.125).
  
  
  CONCLUSIONS
  Our results suggest that CAD significantly improves the sensitivity of PE detection for inexperienced readers with a small but appreciable increase in the rate of false positives.
authors.affiliation: Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226 25 Courtenay Drive, Charleston, SC 29401, USA.
authors.forename: Kevin N
authors.initials: KN
authors.lastname: Blackmon
authors.forename: Charles
authors.initials: C
authors.lastname: Florin
authors.forename: Luca
authors.initials: L
authors.lastname: Bogoni
authors.forename: Joshua W
authors.initials: JW
authors.lastname: McCain
authors.forename: James D
authors.initials: JD
authors.lastname: Koonce
authors.forename: Heon
authors.initials: H
authors.lastname: Lee
authors.forename: Gorka
authors.initials: G
authors.lastname: Bastarrika
authors.forename: Christian
authors.initials: C
authors.lastname: Thilo
authors.forename: Philip
authors.initials: P
authors.lastname: Costello
authors.forename: Marcos
authors.initials: M
authors.lastname: Salganicoff
authors.forename: U
authors.initials: U
authors.lastname: Joseph Schoepf
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011361:Professional Competence; D011651:Pulmonary Artery / Q000000981:diagnostic imaging*; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013022:South Carolina; D014057:Tomography, X-Ray Computed / Q000379:methods*