Abstract #3500

How easily can an existing stroke outcome deep learning model become attuned to new acquisition protocols and patient cohorts?

Anne Nielsen^1,2, Mikkel Bo Hansen¹, Soren Christensen³, Maarten Lansberg³, Greg Zaharchuk⁴, and Kim Mouridsen¹

¹Center of Functionally Integrative Neuroscience and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, ²Cercare Medical, Aarhus, Denmark, ³Department of Neurology, Stanford University, Stanford, CA, United States, ⁴Department of Radiology, Stanford University, Stanford, CA, United States

Acute ischemic stroke is a major disease and one of the leading causes of adult death and disability. Final outcome prediction is hampered by the heterogeneity and physiological complexity of stroke progression. Convolutional neural networks have shown promising results in final outcome predictions. However, less attention has been paid to the generalizability of the results across patient cohorts. We test the applicability of an existing neural network trained on two clinical studies to completely independent cohort from the DEFUSE 2 trial. We examine how a few additional patients can be used to obtain performance comparable to the original studies.

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