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Abstract #3500

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

Anne Nielsen1,2, 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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