The AI-based application demonstrated high performance for the detection of cases with ICH and LVO. This high performance was consistent among ICH subtypes and LVO segments. Similarly, the software presented a high per-region accuracy for the computation of the ASPECT score and all the ASPECTS regions had individual accuracies higher than 81%. These results indicate not only that the algorithm performs adequately but that its high accuracy is generalizable across heterogeneous subgroups of stroke types.  

Regarding IS, the algorithm was capable of correctly classifying almost 90% of the cases with ASPECTS ≥ 6. This cutoff point is crucial for patient eligibility for adequate treatment. Indeed, according to the American Stroke Association guidelines, patients with ASPECTS ≥ 6 should be prioritized for thrombectomy treatment instead of intravenous thrombolysis because it is associated with better patient outcome [8]. Hence, this reliable AI system can be used to accurately select patient management. 

In conclusion, this promising tool may enhance stroke imaging workflow and patient care. Indeed, by assisting radiologists with accurate and automated assessments of ischemic and hemorrhagic stroke, this device may improve their efficiency, increase patient throughput and provide good patient care. Future studies in larger prospective cohorts may confirm the utility of the device on clinical practice and the direct impact on patient outcome.