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Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.Alzheimer disease (AD), vascular dementia (VD), or both occur in 70-90% of all dementias and are often clinically difficult to distinguish. Brain imaging is an important part of the diagnostic evaluation (1-4). However, the brain image may be qualitatively difficult to distinguish between normal and demented persons, and more so between different dementia types such as AD and VD. Since disturbances of brain metabolism may precede structural changes, metabolic images can detect more subtle patterns of change earlier than anatomical ones. We chose single photon emission with computed tomography (SPECT) because (i) it provides measures of regional tissue uptake (hexamethylphenylethyleneamineoxime technetium-99 or HMPAO-99Tc) that show complex patterns of brain activity and because (ii) it is widely available. Improvements in the recognition, quantification, and classification of these image patterns could increase diagnostic sensitivity between normal and abnormal as well as between different subtypes of dementia.The need for better methods of diagnostic interpretation of brain images is illustrated by the large variability in their diagnostic interpretation by even well-trained neuroradiologists using standardized criteria. For example, the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) conducted an interrater reliability study among 14 participating CERAD neuroradiologists for interpreting specific mag-The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact. netic resonance imaging features and found within-feature correlations ranging from 0.64 (for detecting cerebral sulcal dilatation) to 0.79 (for rating the size of the lateral and third ventricl...

We propose in this paper a new unsupervised neural network which is capable of clustering a set of experimental data according to a given generic interpoint similarity measure, and then assign to each new input its appropriate cluster label. The network is able to do this for clusters of any shape, and without knowing in advance the number of clusters to be created. We call this new two-layer network a cluster-detection-and-labeling (CDL) network. In the CDL network the concept of similarity and closeness with regard to distance are combined. Specifically, clusters are represented by a set of prototypes, and the similarities between an input vector and these prototypes are calculated as the inner products of these vectors compared to some thresholds. These thresholds, which depend on the distance between the input vector and the prototype, are calculated in a separate threshold calculating unit. During clustering, the data are cycled through the network several times. At the end of each cycle the clusters are evaluated, and only those with more than a specified number of samples are retained. The others are fed back to be reclustered by an updated network. This process terminates according to a suitable criterion, such as when a prespecified portion of the data are classified. The performance of the CDL network has been compared with that of the winner-take-all (WTA) network for several different cluster structures, since the latter is widely used in cluster analysis applications. These studies demonstrate that the new network performs well for all the tested cluster shapes, also for those cases where the WTA network completely fails.