Purpose: Recently developed machine-learning algorithms have demonstrated strong performance in the detection of intracranial hemorrhage ICH and large vessel occlusion LVO. However, their generalizability is often limited by geographic bias of studies.
The aim of this study was to validate a commercially available deep learning-based tool in the detection of both ICH and LVO across multiple hospital sites and vendors throughout the U. Materials and Methods: This was a retrospective and multicenter study using anonymized data from two institutions. Ground truth was based off imaging interpretations from two board-certified neuroradiologists. Results: There were positive and negative ICH cases. Accuracy was There were positive and negative LVO cases.
The LVO tool demonstrated an accuracy of A subset of 55 randomly selected cases were also assessed for LVO detection at various sites, including the distal internal carotid artery, middle cerebral artery M1 segment, proximal middle cerebral artery M2 segment, and distal middle cerebral artery M2 segment with an accuracy of While some limitations were identified, specifically in the detection of small ICH and distal M2 occlusion, this study highlights a deep learning tool that can assist radiologists in the detection of emergent findings in a variety of practice settings.
Timely diagnosis of acute cerebrovascular disease is critical to reduce patient mortality and morbidity. Two forms of stroke, intracranial hemorrhage ICH and ischemic stroke due to large vessel occlusion LVO are especially devastating. ICH day mortality has been reported at Prompt intervention of these entities is critical in achieving improved outcomes. For example, ICH hematoma expansion was significantly reduced with early blood pressure control 3.
Regarding LVO, functional independence decreased with every hour delay to endovascular thrombectomy 4. Deep learning, a subset of artificial intelligence, has recently emerged as a means to aid clinicians in the timely diagnosis of both ICH and LVO. Newly developed algorithms have demonstrated strong performance in the detection of each 5 β However, limitations of most of these studies are that they are often performed at a single institution and have not been validated in different settings.
