This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep neural network learned representations to provide enriched prediction of the disease based on MRI and or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the generated learned representations between data from different medical environments. Results are presented using a recently published database of Parkinson's related information, which was generated and evaluated in a hospital environment.
A Parkinson's database comprising MRI and DaT Scan data from 78 subjects, 55 patients with Parkinson's and 23 non patients, has been recently released @cite_14 ; it includes, in total 41528 MRI data (31147 from patients and 10381 from non patients) and 925 DaT scans (595 and 330 respectively). Our developments next are based on this database.
@cite_14
1605218991 2806555584 2126496021 2074037951
Availability of multi-modal magnetic resonance imaging (MRI) databases opens up the opportunity to synthesize different MRI contrasts without actually acquiring the images. In theory such synthetic images have the potential to reduce the amount of acquisitions to perform certain analyses. However, to what extent they can substitute real acquisitions in the respective analyses is an open question. In this study, we used a synthesis method based on patch matching to test whether synthetic images can be useful in segmentation and inter-modality cross-subject registration of brain MRI. Thirty-nine T1 scans with 36 manually labeled structures of interest were used in the registration and segmentation of eight proton density (PD) scans, for which ground truth T1 data were also available. The results show that synthesized T1 contrast can considerably enhance the quality of non-linear registration compared with using the original PD data, and it is only marginally worse than using the original T1 scans. In segmentation, the relative improvement with respect to using the PD is smaller, but still statistically significant. Automatic extraction of features from MRI brain scans and diagnosis of Alzheimer's Disease (AD) remain a challenging task. In this paper, we propose an efficient and simple three-dimensional convolutional network (3D ConvNet) architecture that is able to achieve high performance for detection of AD on a relatively large dataset. The proposed 3D ConvNet consists of five convolutional layers for feature extraction, followed by three fully-connected layers for AD NC classification. The main contributions of the paper include: (a) propose a novel and effective 3D ConvNet architecture; (b) study the impact of hyper-parameter selection on the performance of AD classification; (c) study the impact of pre-processing; (d) study the impact of data partitioning; (e) study the impact of dataset size. Experiments conducted on an ADNI dataset containing 340 subjects and 1198 MRI brain scans have resulted good performance (with the test accuracy of 98.74 , 100 AD detection rate and 2,4 false alarm). Comparisons with 7 existing state-of-the-art methods have provided strong support to the robustness of the proposed method. Background Individual magnetic resonance imaging (MRI) disease severity measures, such as atrophy or lesions, show weak relationships to clinical status in patients with multiple sclerosis (MS). Objective To combine MS-MRI measures of disease severity into a composite score. Design Retrospective analysis of prospectively collected data. Setting Community-based and referral subspecialty clinic in an academic hospital. Patients A total of 103 patients with MS, with a mean (SD) Expanded Disability Status Scale (EDSS) score of 3.3 (2.2), of whom 62 (60.2 ) had the relapsing-remitting, 33 (32.0 ) the secondary progressive, and 8 (7.8 ) the primary progressive form. Main Outcome Measures Brain MRI measures included baseline T2 hyperintense (T2LV) and T1 hypointense (T1LV) lesion volume and brain parenchymal fraction (BPF), a marker of global atrophy. The ratio of T1LV to T2LV (T1:T2) assessed lesion severity. A Magnetic Resonance Disease Severity Scale (MRDSS) score, on a continuous scale from 0 to 10, was derived for each patient using T2LV, BPF, and T1:T2. Results The MRDSS score averaged 5.1 (SD, 2.6). Baseline MRI and EDSS correlations were moderate for BPF, T1:T2, and MRDSS and weak for T2LV. The MRDSS showed a larger effect size than the individual MRI components in distinguishing patients with the relapsing-remitting form from those with the secondary progressive form. Models containing either T2LV or MRDSS were significantly associated with disability progression during the mean (SD) 3.2 (0.3)–year observation period, when adjusting for baseline EDSS score. Conclusion Combining brain MRI lesion and atrophy measures can predict MS clinical progression and provides the basis for developing an MRI-based continuous scale as a marker of MS disease severity. Objective: We describe the sampling, initial evaluation, and final diagnostic classification of subjects enrolled in a natural history study of Alzheimer's disease (AD). Design: Volunteer cohort study. Setting: Multidisciplinary behavioral neurology research clinic. Patients or Other Participants: Three-hundred nineteen individuals were enrolled in the Alzheimer Research Program between March 1983 and March 1988. Of these, 204 were originally classified with AD, 102 were normal elderly control subjects, and 13 were considered special cases. Main Outcome Measures: Final consensus clinical diagnosis, final neuropathologic diagnosis, and death. Results: Of the 204 patients enrolled in the study, re-review after as many as 5 years of follow-up resulted in a final clinical classification of 188 with probable AD. Seven patients were believed to have a significant vascular component to the dementia, three were found to have developed depression, and six were excluded on other clinical grounds. Neuropathologic examination of 50 brains indicated definite AD in 43. After removing these seven misdiagnosed patients, the final group of probable definite AD totaled 181 individuals. Accuracy of the baseline clinical diagnosis relative to neuropathology was 86 , and when follow-up clinical data were considered, 91.4 . Detailed neuropsychological testingyielded high sensitivity (0.988) and specificity (0.983) to dementia. Analyses of survival time from study entry until death revealed that older patients were significantly more likely to die during follow-up, but neither sex, years of education, nor pattern of cognitive impairment were related to survival. Conclusions: These data provide the descriptive basis for future studies of this cohort. They indicate that longitudinal follow-up of demented cases increases accuracy of diagnosis, and that detailed cognitive testing aids in early classification.