Pattern recognition methods for MRS analysis and classification

It is clear that statistical classification of MRS has considerable potential as an accurate diagnostic advice tool. However, sample sizes are not yet sufficient to guarantee performance in large-scale clinical trials. In addition, the most commonly used classification strategies, namely Principal Components Analysis of in vitro spectra and Linear Discriminants Analysis of in vivo data, while offering considerable accuracy, are not optimal even within the currently available linear statistical classifiers. The issue of non-linearity and the consequent need of neural network analysis remains rests also on the outcome of larger studies, although the performance of these methods has already been shown to be competitive with that of statistical methods. Further work on principled methods for neural network design and rule-extraction offer the potential, in the near future, for high-performing, transparent non-linear classifiers. Alternative algorithms can also be used for automatic labelling, of clustering, of spectra. Overall, the results of the preliminary studies reported here are encouraging both for the accuracy that has been achieved and the consistency of the variable relevance ranking with clinical expectation