A neural network classifier for the automatic interpretation of epileptogenic zones in F-18FDG brain PET

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed a computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients which were diagnosed as normal (n=64), left temporal lobe epilepsy (n=112), or right temporal robe epilepsy (n=81) by visual interpretation. Automatically segmented volumes of interest (VOI) were used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16 mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOI for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feedforward error backpropagation neural network classifier with 17 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5/spl sim/40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75/spl sim/80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful tool as clinical decision supporting tool for the localization of epileptogenic zones.