Discrimination between parkinsonian syndrome and essential tremor using artificial neural network classification of quantified DaTSCAN data.

BACKGROUND: In the semi-quantitative assessment of DaTSCAN images, it has been suggested that the ratio of tracer accumulation in the putamen to that in the caudate nucleus may be helpful and could allow parkinsonian syndromes progression to be assessed. Separation of ratio values has been reported when early Parkinson's disease is compared with essential tremor. The separation is lost, however, when the Parkinson's disease is not early stage.

OBJECTIVES: To evaluate whether a two-stage analysis can differentiate between parkinsonian syndromes, of various stages, and essential tremor, and whether such a two-stage analysis can be undertaken in a single step using artificial neural networks (ANNs).

METHODS: Data from 18 patients were analysed. Quantification was undertaken by manually drawing irregular regions of interest (ROIs): over each caudate nucleus and putamen and over an occipital cortex area near the posterior edge of the brain. A two-stage analysis was undertaken and was repeated, in a single step, using an ANN.

RESULTS: The first stage, of the two-stage analysis, identified 12 patients with non-early parkinsonian syndromes. The remaining six patients were then successfully classified into early parkinsonian syndromes and essential tremor. The ANN analysis successfully discriminated parkinsonian syndromes from essential tremor, in all patients, in a single step.

CONCLUSIONS: The two-stage process provides a method for classifying early disease without being compromised by the noise from non-early disease. The results of the single stage ANN analysis were very definite and it may be considered to have potential in the quantification of DaTSCAN images for clinical use.