Use of neural networks in brain SPECT to diagnose Alzheimer's disease

Abstract

The usefulness of artificial neural networks in the classification of 99mTc-HMPAO SPECT axial brain scans was investigated in a study group of Alzheimer's disease patients and age-matched normal subjects.

Methods: The cortical circumferential profiling (CCP) technique was used to extract information regarding patterns of cortical perfusion. Traditional analysis of the CCP data, taken from slices at the level of the basal ganglia, indicated significant perfusion deficits for Alzheimer's disease patients relative to normals, particularly in the left temporo-parietal and left posterior frontal areas of the cortex. The compressed profiles were then used to train a neural-network classifier, the performance of which was compared with that of a number of more traditional statistical (discriminant function) techniques and that of two expert viewers.

Results: The optimal classification performance of the neural network (ROC area = 0.91) was better than that of the alternative statistical techniques (max. ROC area = 0.85) and that of the expert viewers (max. ROC area = 0.79).

Conclusion: The CCP produces perfusion profiles which are well suited to automated classification methods, particularly those employing neural networks. The technique has the potential for wide application.