Abstract #0702

Deep Learning Based Algorithm to Identify Large Vessel Stenosis and Occlusion on Contrast Agent-free Magnetic Resonance Imaging

Di Wu¹, Mengzhou Sun², Yi Li³, Xiaoyun Liang³, Feng Huang³, and Wenzhen Zhu¹

¹Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ²Neusoft Medical Systems Co. Ltd, Shenyang, Liaoning, China, Beijing, China, ³Neusoft Medical Systems Co. Ltd, Shenyang, Liaoning, China, Shanghai, China

Synopsis

Keywords: Stroke, Machine Learning/Artificial IntelligenceLarge vessel occlusion detection based on clinical scales is of low sensitivity and that based on CTA needs contrast agent exposure. This study aims to develop a deep learning (DL) algorithm for detecting intracranial large vessel steno-occlusion on contrast agent-free MR techniques including DWI and ASL. The accuracy of the DL algorithm was 88.2% with a sensitivity of 88.0%, comparable to CTA-based DL algorithms with sensitivity ranging from 67% to 94%. The MR-based DL algorithm is feasible to accurately detect intracranial large vessel steno-occlusion without intervention, radiation exposure and contrast agent, which could optimize stroke workflow and guide clinical decision-making.

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