## 4 Discussion  
The introduction of MT revolutionized the treatment of ischemic stroke, significantly improving outcomes. This therapy became the standard of care in patients with occlusion of major arteries supplying the brain, termed large vessel occlusion (LVO). As the baseline inclusion criterion for MT is the presence of an LVO, its accurate and rapid detection is necessary. Routinely, CTA is used for this purpose as a part of diagnostic stroke protocols. However, its quick and accurate assessment targeted at LVO's detection needs expertise and relies on the learning curve. The most competent assessment is guaranteed by experienced neuroradiologists. However, as they are usually accessible in comprehensive stroke centers performing MT, the majority of stroke patients primarily are transported to the nearest stroke unit without a neuroradiologist present. Therefore, deciding whether a patient is eligible for MT is frequently challenging in primary stroke units. In the last few years, several vendors developed AI-based software packages assisting radiologists in the selection of stroke patients for MT, particularly in LVO detection: e-STROKE (Brainomix Ltd., Oxford, UK), RAPID CTA (SchemaView, Menlo Park, CA, USA), and Viz LVO (Viz.ai, San Francisco, CA, USA). We tested the performance of e-CTA, a part of the e-STROKE package, for LVO detection using an experienced neuroradiologist's reading as the reference. Our results \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Occlusion Localization**}} & \multicolumn{2}{c}{**e-CTA**} & \multicolumn{2}{c}{**Medical Students**} \\ \cline{2-6}  & **Falsely Detected** & **Missed LVOs** & **Falsely Detected** & **Missed LVOs** & **Missed LVOs** \\ \hline \multirow{4}{*}{**Proximal**} & **ICA (n = 14)** & — & 2 & 3 & — \\ \cline{2-6}  & **ICA+MCA-M1 (n = 7)** & — & 1 & — & — \\ \cline{2-6}  & **MCA-M1 (n = 24)** & 2 & 4 & 4 & 3 \\ \hline
**Distal** & **M2 (n = 25)** & — & 16 & 3 & 14 \\ \hline
**Overall false results** & 2/38 (5.3\%) & 23/70 (32.9\%) & 10/38 (26.3\%) & 17/70 (24.3\%) \\ \hline
**Overall sensitivity** & & 0.67 (0.55–0.78) & & 0.76 (0.64–0.85) \\ \hline
**Overall specificity** & & 0.95 (0.82–0.99) & & 0.74 (0.57–0.87) \\ \hline
**Overall PPV** & & 0.96 (0.86–0.99) & & 0.84 (0.73–0.92) \\ \hline
**Overall NPV** & & 0.61 (0.47–0.73) & & 0.62 (0.47–0.76) \\ \hline
**Overall accuracy** & & 0.77 (0.68–0.84) & & 0.75 (0.66–0.83) \\ \hline \hline \end{tabular}  
Notes: 95% CI are provided in brackets.  
\end{table} 
Table 2: Distribution of proximal, distal, and overall LVOs falsely detected and missed by e-CTA and medical students with overall sensitivity, specificity, PPV, NPV, and accuracy. show low overall sensitivity of 0.67 and accuracy of 0.77 for e-CTA in LVO detection, especially for distal occlusions--0.36 and 0.71, respectively. The sensitivity of 0.84 for proximal LVOs could be considered satisfactory. However, the sensitivity of 0.36 for distal LVOs should be regarded as insufficient. The e-CTA missed 32.9% LVOs. The majority of missed occlusions were localized distally. This means that \({}^{1}/_{3}\) of stroke patients with LVO would be falsely disqualified from MT by e-CTA, which must be declared inadmissible. Possibly, e-CTAs performance could be improved by a more comprehensive and more precise training on large datasets containing a wide range of real-world CT scans from stroke patients (focused on cases with distal LVOs) and negative controls, with ground-truth data from additional imaging such as MRI and CT, along with other modalities and clinical information. These datasets should contain examples of scans captured with scanners from all major manufacturers from a wide range of countries worldwide. Noteworthy, in our study, the last year medical students after 2 h training reached similar sensitivity and accuracy but much lower specificity compared to AI-based software. Such a constellation may be caused by e-CTA's settings which prefer high specificity at the expense of sensitivity.