## 4 Discussion
Because our results were obtained by the joined result of both CNN's, we were not able to create an AUC similar to [PERSON] et al. and directly compare the results. More specifically, since the two CNN's each have their own threshold, these two thresholds would result in a 3D type of AUC. To compare our results, we computed the Youden's J statistic for our results and made an estimation of this statistic from the AUC presented byLisowska et al. From this we were able to conclude that our network outperformed their network. More details on this comparison can be found within the Supplementary Materials (Analysis S1). The presented CNN has the potential to be used in clinical practice to support non-experts in thrombus detection on baseline NCCT. The detection of thrombus on NCCT has several benefits. First, it can speed up the clinical workup, as NCCT is always performed first. When the thrombus is clearly detected on NCCT, the patient can be immediately sent to a comprehensive stroke center and the intervention team can already be informed before CTA imaging is performed and analyzed. In addition, it could be beneficial for smaller community hospitals, which operate in less developed settings to detect LVOs, without the need for an additional CTA. The CNN could be incorporated within the current workflow. The application of our network requires registration and pre-processing steps as described in the methods section. With the appropriate hardware typical analysis time is less than 2 min. Still, the presented results showed that in about 15% of patients the thrombus is missed and approximately 35% of the detected thrombi are actual false positives. Therefore, this network is not yet accurate enough to replace experts and should be considered a support tool. Moreover, in current practice, all patients with a clinical suspicion of an LVO, require a CTA. Before such a CNN could be integrated within daily clinical practice, its value should be assessed in a prospective study to determine if the use of the CNN advances correct thrombus detection, faster treatment and reduction in workload of expert neuroradiologists. The future availability of accurate thrombus volume segmentation may support the analysis of thrombus image characteristics in large image data sets present in randomized controlled trials. Thromus density, perviousness and volume have previously been associated with functional outcome and recanalization [26; 27; 32]. Currently, thrombus image characteristics are analyzed using semi-manual annotation, which is tedious, time consuming and is prone to observer variability. This makes analysis of large number of images less feasible. In contrast, automated segmentation allows for fast segmentation and thrombus biomarker extraction. Automated accurate thrombus volume segmentation may also support machine learning approaches for the extraction of histological information, based on thrombus image characteristics. For example, [PERSON] et al. [33] showed that thrombus density might characterize thrombi as blood-cell rich or fibrin-rich, indicating whether a thrombus would be susceptible for intravenous thrombolysis. In future work, the CNN could be fed with histological information in combination with the segmentation and location of the thrombus to train for thrombus histology information extraction from imaging data alone. This study suffers some limitations. First, the sample size of this study was limited. CNN networks require large number of samples to obtain a good accuracy. The more samples are included for training, the more generalizable the CNN will be, since it will be trained on a larger variation of thrombus cases. Since our main goal was to evaluate the accuracy of a tool for thrombus detection and the thrombus segmentations were merely evaluated for exploration for potential future work, we think that the current training dataset is sufficient enough for this proof of concept. We thereby kept track of the training and validation accuracy to prevent from overfitting.