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.