Computer-aided stenosis detection at coronary CT angiography: effect on performance of readers with different experience levels

Abstract

Objectives: To evaluate the effect of a computer-aided detection (CAD) algorithm for coronary CT angiography (cCTA) on the performance of readers with different experience levels.

Methods: We studied 50 patients (18 women, 58 ± 11 years) who had undergone cCTA and quantitative coronary angiography (QCA). Eight observers with varying experience levels evaluated all studies for ≥50 % coronary artery stenosis. After 3 months, the same observers re-evaluated all studies, this time guided by a CAD system. Their performance with and without the CAD system (sensitivity, specificity, positive predictive value and negative predictive value) was assessed using the Likelihood Ratio Χ(2) test both at the per-patient and per-vessel levels.

Results: The sensitivity of the CAD system alone for stenosis detection was 71 % per-vessel and 100 % per-patient. There were 54 false positive (FP) findings within 199 analyzed vessels, most of them associated with non-obstructive (<50 %) lesions. With CAD, one (out of three, 33 %) inexperienced reader's per-patient sensitivity and negative predictive value significantly improved from 79 % to 100 % (P = 0.046) and from 90 % to 100 % (P = 0.034), respectively. Other readers' performance indices showed no statistically significant change.

Conclusions: Our results suggest that CAD can improve some inexperienced readers' sensitivity for diagnosing coronary artery stenosis at cCTA.