A method for computer-aided triage, the method comprising, at a processing system: • receiving a data packet comprising a set of computed tomography, CT, images of a brain of the patient sampled at the first point of care, wherein the data packet is concurrently sent to a standard radiology workflow operating in parallel with the method; • determining a large vessel region from the set of CT images; • extracting a large vessel centerline from the large vessel region; • calculating a centerline length of the large vessel centerline; • comparing the centerline length with a predetermined threshold; • detecting a large vessel occlusion when the centerline length is less than the predetermined threshold; • in response to large vessel occlusion detection: ▪ presenting a notification on a mobile device associated with a specialist from the standard radiology workflow, the specialist associated with a second point of care; and ▪ displaying a compressed version of the set of CT images on the mobile device in response to interaction with the notification • wherein determining a large vessel region comprises: ▪ extracting soft matter regions from the set of CT images; ▪ registering the soft matter regions to a reference series having a target vessel region; ▪ after registration, probabilistically segmenting the soft matter regions falling within the target vessel region; and ▪ applying a binary mask to the probabilistically segmented soft matter regions falling within the target region, wherein voxels having probabilities above a threshold probability are included in the large vessel region. • receiving a data packet comprising a set of computed tomography, CT, images of a brain of the patient sampled at the first point of care, wherein the data packet is concurrently sent to a standard radiology workflow operating in parallel with the method; • determining a large vessel region from the set of CT images; • extracting a large vessel centerline from the large vessel region; • calculating a centerline length of the large vessel centerline; • comparing the centerline length with a predetermined threshold; • detecting a large vessel occlusion when the centerline length is less than the predetermined threshold; • in response to large vessel occlusion detection: ▪ presenting a notification on a mobile device associated with a specialist from the standard radiology workflow, the specialist associated with a second point of care; and ▪ displaying a compressed version of the set of CT images on the mobile device in response to interaction with the notification ▪ presenting a notification on a mobile device associated with a specialist from the standard radiology workflow, the specialist associated with a second point of care; and ▪ displaying a compressed version of the set of CT images on the mobile device in response to interaction with the notification • wherein determining a large vessel region comprises: ▪ extracting soft matter regions from the set of CT images; ▪ registering the soft matter regions to a reference series having a target vessel region; ▪ after registration, probabilistically segmenting the soft matter regions falling within the target vessel region; and ▪ applying a binary mask to the probabilistically segmented soft matter regions falling within the target region, wherein voxels having probabilities above a threshold probability are included in the large vessel region. ▪ extracting soft matter regions from the set of CT images; ▪ registering the soft matter regions to a reference series having a target vessel region; ▪ after registration, probabilistically segmenting the soft matter regions falling within the target vessel region; and ▪ applying a binary mask to the probabilistically segmented soft matter regions falling within the target region, wherein voxels having probabilities above a threshold probability are included in the large vessel region. <EOS>