Study Suggests AI Enhances Non-Contrast CT Detection of Large Vessel Occlusion

An emerging artificial intelligence (AI) algorithm may be beneficial in facilitating earlier detection of large vessel occlusions on non-contrast computed tomography (CT) scans and subsequent identification of stroke patients who are good candidates for a minimally invasive thrombectomy. In a study abstract presentation at the Society of Neurointerventional Surgery's (SNIS) 19th Annual Meeting in Toronto, researchers noted that the AI algorithm detects clinical symptoms of ipsiversive gaze deviation on non-contrast CT and was trained with 200 CT scans. In a subsequent study of 116 patients who received endovascular therapy for large vessel occlusions, the study authors found an ipsiversive gaze deviation in 71.1 percent of patients (59 out of 83 patients) with proximal occlusions and the AI algorithm had a 79 percent accuracy rate (47 out of 59 patients) in identifying ipsiversive gaze deviation. The study authors said the AI algorithm could result in more expeditious treatment decisions for patients with acute ischemic stroke. "Simply put, the faster we act, the better our stroke patients' outcomes will be. Our results represent an advance that has the potential to speed up the identification of (large vessel occlusion) stroke during the triage process at the hospital," emphasized lead study author Jason Tarpley, M.D., Ph.D, the stroke medical director at the Pacific Stroke and Aneurysm Center in Santa Monica, Ca.