Automated computer-aided stenosis detection at coronary CT angiography: initial experience.
OBJECTIVE
To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).


METHODS
We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.


RESULTS
QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.


CONCLUSIONS
Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.
Arnoldi
Elisabeth
E
Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, MSC 226, Charleston, SC 29401, USA.
Gebregziabher
Mulugeta
M
Schoepf
U Joseph
UJ
Goldenberg
Roman
R
Ramos-Duran
Luis
L
Zwerner
Peter L
PL
Nikolaou
Konstantin
K
Reiser
Maximilian F
MF
Costello
Philip
P
Thilo
Christian
C
D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*