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Abstract Background The reduction of processing times in the treatment of acute ischemic stroke is of outstanding importance. Our objective is to analyze the acute stroke care chain from onset to treatment in a city in Germany comprising three stroke units. Additionally, we discuss solutions for detected treatment delays. Methods We conducted an in-depth analysis of acute stroke care processing times across three local stroke centers in Düsseldorf among all emergency services transportations for suspected stroke. Isochrone mapping was performed to identify areas with prolonged transportation times. Results Among the 1,714 transportations, 943 patients had confirmed strokes. Prehospital care constituted 58% of total emergency care time until imaging. Patients with confirmed stroke had reduced in-hospital times while patients receiving treatment experienced faster in-hospital times. Isochrone mapping revealed disparities in transportation times within the city. Conclusions In conclusion, we identified confirmation of stroke symptoms as pre- and in-hospital and treatment eligibility as in-hospital process accelerators in stroke care. We propose the introduction of an in-ambulance video consulting model to accelerate contact to stroke-experts and accelerate processing times for patients eligible for treatment. Furthermore, we discuss the combination of in-ambulance video consulting with imaging and starting treatment outside traditional stroke centers, followed by transportation to a stroke center during thrombolysis, which might further accelerate treatment in specific cases.

Abstract This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model’s transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach.