Evaluation of deep convolutional neural networks for detection of freezing of gait in Parkinson’s disease patients

2018 ◽  
Vol 46 ◽  
pp. 221-230 ◽  
Author(s):  
Yi Xia ◽  
Jun Zhang ◽  
Qiang Ye ◽  
Nan Cheng ◽  
Yixiang Lu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Freezing Of Gait ◽  
Deep Convolutional Neural Networks

scholarly journals 3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

2021 ◽  
Author(s):  
Nikhil J. Dhinagar ◽  
Sophia I. Thomopoulos ◽  
Conor Owens-Walton ◽  
Dimitris Stripelis ◽  
Jose Luis Ambite ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Brain Mri

scholarly journals Parkinson's Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks

2019 ◽  
Vol 13 ◽  
Author(s):  
Andrés Ortiz ◽  
Jorge Munilla ◽  
Manuel Martínez-Ibañez ◽  
Juan M. Górriz ◽  
Javier Ramírez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Disease Detection

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

2019 ◽  
Vol 29 (09) ◽  
pp. 1950010 ◽  
Author(s):  
Octavio Martinez Manzanera ◽  
Sanne K. Meles ◽  
Klaus L. Leenders ◽  
Remco J. Renken ◽  
Marco Pagani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Positron Emission Tomography ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Characteristic Curve ◽  
Emission Tomography ◽  
Imaging Data ◽  
Positron Emission

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

scholarly journals The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 740
Author(s):  
Mahmood Saleh Alzubaidi ◽  
Uzair Shah ◽  
Haider Dhia Zubaydi ◽  
Khalid Dolaat ◽  
Alaa A. Abd-Alrazaq ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Scoping Review ◽  
Neurodegenerative Disorder ◽  
Freezing Of Gait ◽  
Data Types ◽  
Medical Test ◽  
Scoping Reviews

Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

scholarly journals Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

2018 ◽  
Vol 87 ◽  
pp. 67-77 ◽  
Author(s):  
Clayton R. Pereira ◽  
Danilo R. Pereira ◽  
Gustavo H. Rosa ◽  
Victor H.C. Albuquerque ◽  
Silke A.T. Weber ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Disease Identification

scholarly journals High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Franz M. J. Pfister ◽  
Terry Taewoong Um ◽  
Daniel C. Pichler ◽  
Jann Goschenhofer ◽  
Kian Abedinpour ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
State Detection
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