Toward Rapid Stroke Diagnosis with Multimodal Deep Learning
Mingli Yu (1), Tongan Cai (1), Xiaolei Huang (1), Kelvin Wong (2),
John Volpi (3), James Z. Wang (1), and Stephen T.C. Wong (2)
(1) The Pennsylvania State University, University Park, Pennsylvania, USA
(2) TT and WF Chao Center for BRAIN & Houston Methodist Cancer Center,
Houston Methodist Hospital, Houston, Texas, USA
(3) Eddy Scurlock Comprehensive Stroke Center, Department of Neurology,
Houston Methodist Hospital, Houston, Texas, USA
Abstract:
Stroke is a challenging disease to diagnose in an emergency room (ER)
setting. While an MRI scan is very useful in detecting ischemic
stroke, it is usually not available due to space constraint and high
cost in the ER. Clinical tests like the Cincinnati Pre-hospital Stroke
Scale (CPSS) and the Face Arm Speech Test (FAST) are helpful tools
used by neurologists, but there may not be neurologists immediately
available to conduct the tests. We emulate CPSS and FAST and propose a
novel multimodal deep learning framework to achieve computer-aided
stroke presence assessment over facial motion weaknesses and speech
inability for patients with suspicion of stroke showing facial
paralysis and speech disorders in an acute setting. Experiments on our
video dataset collected on actual ER patients performing specific
speech tests show that the proposed approach achieves diagnostic
performance comparable to that of ER doctors, attaining a 93.12%
sensitivity rate while maintaining 79.27% accuracy. Meanwhile, each
assessment can be completed in less than four minutes. This
demonstrates the high clinical value of the framework. In addition,
the work, when deployed on a smartphone, will enable selfassessment by
at-risk patients at the time when stroke-like symptoms emerge.
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Citation:
Mingli Yu, Tongan Cai, Xiaolei Huang, Kelvin Wong, John Volpi, James
Z. Wang and Stephen T.C. Wong, ``Toward Rapid Stroke Diagnosis with
Multimodal Deep Learning,'' Proceedings of the International
Conference on Medical Image Computing and Computer Assisted
Interventions, pp. 616-626, Virtual, October 2020.
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Last Modified:
October 15, 2020
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