Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).
Journal:
Hellenic journal of nuclear medicine
PMID:
29324935
Abstract
OBJECTIVE: The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using I-FP-CIT brain SPET data, a classification tree (CIT), was applied. CIT is an automatic classifier composed of a set of logical rules, organized as a decision tree to produce an optimised threshold based classification of data to provide discriminative cut-off values. We applied a CIT to I-FP-CIT brain SPET semiquantitave data, to obtain cut-off values of radiopharmaceutical uptake ratios in caudate nuclei and putamina with the aim to diagnose PD versus other conditions.