## 4 Discussion
It is worth referring to the overdetection of MCA-M1 occlusions by medical students, with a total of 36, vs. 24 revealed by the neuroradiologist. However, students found significantly fewer ICA (8 vs. 14) and MCA-M2 (10 vs. 25) occlusions compared to neuroradiologist. This could be explained by students mistakenly classifying some ICA occlusions as proximal MCA-M1 while some proximal MCA-M2 occlusions as distal MCA-M1. These mistakes were presumably caused by their limited experience assessing complicated anatomy of the cerebral arteries in CTA images. We found only a single study testing the same software published by [PERSON] et al., who found a higher overall sensitivity of 0.84 in a group of 144 stroke patients [7]. The performance of e-CTA in both studies is similar for proximal LVO. In this group, the sensitivity reported by [PERSON] et al. was 0.93 vs. 0.84 in our study, and their accuracy was 0.97 vs. 0.89 in our study [7]. This discrepancy could be explained by a different definition of proximal and distal LVOs used in both papers. [PERSON] et al. limited proximal LVO to the initial part of MCA-M1 up to its anterior temporal branch, while the continuation of MCA-M1 beyond the anterior temporal branch was classified as distal LVO [7]. We used a different approach based on anatomy, as described in the methods section. The added value of our study is a more comprehensive analysis by including distal LVOs, which was omitted in the paper published by [PERSON] et al. [7]. It should be emphasized that according to guidelines, MT is recommended in distal LVOs as well  
The only study showing similar results to ours was presented as the conference abstract by [PERSON] et al., who reported the overall sensitivity of 0.66 and 0.39 for distal LVO using Viz LVO [10]. However, other studies evaluating Viz LVO revealed much higher sensitivities of 0.81-0.90 overall, 0.92 for proximal, and 0.54 for distal occlusions [8; 11; 12; 13]. Similarly, two papers evaluating RAPID CTA showed much higher sensitivity values compared to ours. [PERSON] et al. report the sensitivity of 0.92 overall, 0.94 for proximal, and 0.86 for distal LVO detection [4]. [PERSON] et al. show an overall sensitivity of 0.96 [5]. According to the only systematic review published in 2020 by [PERSON] et al., LVO detection studies variably report AI software performance with broad sensitivities of 0.67-0.98 [9]. However, this systematic review is based only on several conference abstracts, as all of the above-mentioned studies were published later. Although all comprehensive platforms by iSchemaView, Viz.ai, and Brainomix are based on a convolutional neural network (CNN) algorithm to detect LVOs, each vendor uses different modifications of this method. These modifications include different software settings that prefer high sensitivity at the expense of specificity or vice versa. Additionally, in RAPID CTA, a CTA vessel density detection feature is used to identify relative distal MCA vessel asymmetries suggestive of an LVO. These differences may impact the results of the evaluation studies causing the above-presented discrepancies. Relatively poor performance of the tested AI-based software in our study, especially for distal LVOs, could be partially explained by the inconsistent definition of proximal and distal LVOs in the literature. Some authors, like [PERSON] et al. and [PERSON] et al. [4; 7], define the vessel segment beyond the anterior temporal branch as a so-called M2 trunk which is classified as distal, while others, like the authors in the present study, regard it as the distal portion of the M1 segment which is classified as proximal. This difference could reduce the sensitivity of e-CTA for distal LVO detection in our study. Lower sensitivity for distal LVO detection compared to proximal could also be caused by the more complex anatomy of MCA-M2 branches, which makes automatic segmentation more challenging; the smaller size of vessels with poorer contrast filling and their greater individual anatomical variability compared to ICA and MCA-M1.