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In International journal of stroke : official journal of the International Stroke Society

BACKGROUND AND AIM : The aim of this study was to assess the diagnostic accuracy of e-CTA (Brainomix) in the automatic detection of large vessel occlusions (LVO) in anterior circulation stroke.

METHODS : Of 487 CT angiographies (CTA) from patients with LVO stroke, 327 were used to train the algorithm while the remaining cases together with 140 negative CTAs were used to validate its performance against ground truth. Of these 301 cases, 144 were randomly selected and used for an additional comparative analysis against 4 raters. Sensitivity, specificity, positive and negative predictive value (PPV and NPV), accuracy and level of agreement with ground truth (Cohenâs Kappa) were determined and compared to the performance of a neuroradiologist, a radiology resident and two neurology residents.

RESULTS : e-CTA had a sensitivity and specificity of 0.84 (0.77-0.89) and 0.96 (0.91-0.98) respectively for the detection of any LVO on the correct side in the whole validation cohort. This performance was identical in the comparative analysis subgroup and was within the range of physicians at different levels of expertise: 0.86-0.97 and 0.91-1.00, respectively. For the detection of proximal occlusions, it was 0.92 (0.84-0.96) and 0.98 (0.94-1.00) for the whole cohort and 0.93 (0.80-0.98) and 1.00 (0.95-1.00) for the comparative analysis, respectively for e-CTA. The range was 0.8-0.97 for sensitivity and 0.97-1.00 for specificity for the four physicians.

CONCLUSIONS : The performance of e-CTA in detecting any LVO is comparable to less experienced physicians, but is similar to experienced physicians for detecting proximal LVOs.

Seker Fatih, Pfaff Johannes Alex Rolf, Mokli Yahia, Berberich Anne, Namias Rafael, Gerry Steven, Nagel Simon, Bendszus Martin, Herweh Christian


Artificial intelligence, CT angiography, CT scan, Ischaemic stroke, Large vessel occlusion, Radiology