Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by 123I-FP-CIT brain SPECT

To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using I-123-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). I-123-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H&Y) score of a parts per thousand currency sign2 (early PD), and 63 with PD with a H&Y score of a parts per thousand yen2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an average performance PNN was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9 +/- 8.1% (mean +/- SD), in patients with advanced PD 78.9 +/- 8.1%, and in ET patients 96.6 +/- 2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5 +/- 3.4%. This means that patients with putamen values > 5.99 were classified as having ET, while patients with putamen values < 5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8 +/- 5.3%), and if the value was < 6.97 patients were classified as having advanced PD (probability 88.1%+/- 8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities.