Acute ischemic stroke caused by large vessel occlusion (LVO) carries high morbidity and mortality, but is readily treatable if diagnosed early. Expeditious diagnosis and triage of LVO patients to hospitals equipped to perform emergency revascularization treatment maximizes the patient’s chance of good clinical outcome. Current clinical LVO screening tools either lack precision or require specialized personnel to perform comprehensive clinical assessment. The objective of the present study is to develop a novel, rapid and automated computer algorithm capable of detecting and predicting signs and likelihood of LVO.
A preliminary model developed with machine learning techniques utilized clinical and imaging data of 300 patients provided by the Hospital Authority. Building on the pilot results, we further fine-tuned the computer algorithm with more patient data and imaging parameters derived from contrast CT-angiograms and CT brain scans interpreted by a team of Neuroradiologists to determine the ‘ground truth’ (i.e., the presence of absence of LVO in individual patients). Deep learning is performed with the information generated to establish a computer algorithm to predict the likelihood of LVO, as well as to assess the feasibility of computer interpretation of CT and CT-angiogram for signs of LVO. Here we present the developmental process and findings of this multi-disciplinary collaborative project, and discuss the potential to apply this innovation in clinical practice.
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