Abstract
As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.
Highlights
Parkinson’s disease (PD) is a long-term degenerative disease of the central nervous system which effects 2–3% of the world’s population over 65 years old, and its incidence is increasing in recent years(Postuma and Berg, 2017)
Brajkovic et al combined visual assessment of individual scans with statistical parametric mapping (Brajkovic et al, 2017). These researches showed that compared with normal control (NC), glucose metabolism of Parkinson’s Disease (PD) patients in sensorimotor cortex, lateral frontal and parietooccipital areas was decreased (Meles et al, 2017), which is of great value for the early diagnose of PD
To determine the optimal structure and parameters of the Group Lasso Sparse Deep Belief Network (GLS-deep belief network (DBN)) model, including the scale parameter, location parameter, overlapping rate, and the number of hidden units, the greedy search algorithm was used in the whole training progress until the average accuracy of the validation dataset was optimized
Summary
Parkinson’s disease (PD) is a long-term degenerative disease of the central nervous system which effects 2–3% of the world’s population over 65 years old, and its incidence is increasing in recent years(Postuma and Berg, 2017). Imaging disease-specific patterns of regional glucose metabolism with 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) allows for accurate diagnosis of PD, this has been increasingly acknowledged in recent years (Eckert et al, 2005; Dabrowska et al, 2015; Meyer et al, 2017; Politis et al, 2017). Brajkovic et al combined visual assessment of individual scans with statistical parametric mapping (Brajkovic et al, 2017). These researches showed that compared with NC, glucose metabolism of PD patients in sensorimotor cortex, lateral frontal and parietooccipital areas was decreased (Meles et al, 2017), which is of great value for the early diagnose of PD
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