Automatic classification and prediction models for early Parkinson's disease diagnosis from SPECT imaging

Authors:
R. Prashanth;Sumantra Dutta Roy;Pravat K. Mandal;Shantanu Ghosh
Affiliations:
Department of Electrical Engineering, Indian Institute of Technology Delhi, India;Department of Electrical Engineering, Indian Institute of Technology Delhi, India;Neurospectroscopy and Neuroimaging Laboratory, National Brain Research Centre, India and Department of Radiology, Johns Hopkins Medicine, MD, USA;Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, MA, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

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Abstract

Early and accurate diagnosis of Parkinson's disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ^1^2^3I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ^1^2^3I-Ioflupane SPECT scans (as obtained from the Parkinson's progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.