Shiliang Huang1, Qiang Shen1,2,
Timothy Q. Duong1,2
1Research Imaging Institute, University
of Texas Health Science Center at San Antonio, San Antonio, TX, United
States; 2Department of Ophthalmology/Radiology, University of
Texas Health Science Center at San Antonio, San Antonio, TX, United States
A
flexible artificial neural network (ANN) algorithm was developed and applied
to predict ischemic tissue fate on three stroke groups: 30-min, 60-min and
permanent MCAO in rats. CBF, ADC and T2 were acquired during the acute phase
up to 3hrs and again at 24hrs followed by histology. Infarct was predicted
pixel-by-pixel using only acute (30-min) stroke data.
Receiver-operating-characteristic analysis was used to quantify prediction
accuracy. It was concluded that the ANN predictive model has the potential to
serve as promising metrics for diagnosis, prognosis and therapeutic
evaluation of acute stroke.