scholarly journals Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7034
Author(s):  
Hui Wen Loh ◽  
Wanrong Hong ◽  
Chui Ping Ooi ◽  
Subrata Chakraborty ◽  
Prabal Datta Barua ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Neurodegenerative Disorder ◽  
Healthcare Services ◽  
Learning Models ◽  
Automated Diagnosis ◽  
Automated Identification ◽  
Cad Tools ◽  
Life Years

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

scholarly journals KARAKTERISTIK KLINIS DAN DERAJAT BERAT GEJALA MOTORIK PENYAKIT PARKINSON DI RSUP SANGLAH DAN RSUD WANGAYA DENPASAR

2020 ◽  
Vol 3 (1) ◽  
pp. 6-11
Author(s):  
Pristanova Larasanti ◽  
Dewa Putu Gede Purwa Samatra ◽  
Sri Yenni Trisnawati ◽  
I Ketut Sumada
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
General Hospital ◽  
Neurodegenerative Disorder ◽  
Cross Sectional ◽  
Disability Adjusted Life ◽  
Life Years ◽  
The Status ◽  
Cross Sectional Design ◽  
Global Burden Disease

Background: Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The Global Burden Disease (GBD) report published in 2018 estimated there were 6.1 million individuals suffering from PD globally and causing 3.2 million Disability-Adjusted Life Years (DALY) and 211,296 deaths in 2016. Disability mainly caused by motor symptoms. This study aims to determine the clinical characteristics and motor severity in PD patients in Sanglah and Wangaya General Hospital Denpasar. Method: Descriptive observational study with cross-sectional design. Samples taken consecutively from all patients diagnosed with PD at Neurology Polyclinic in Sanglah and Wangaya General Hospital from December 2018 - February 2019. Result: From 47 subjects with PD, 72.3% were male, 83% had onset within 1-5 years, and the mean age was 63.87 ± 8.67 years. As many as 44.7% subjects had Hoehn-Yahr 2 stadium, with an average MDS-UPDRS III score of 35.11 ± 21.39, and 48.9% subjects had mild severity. As many as 59.6% subjects had the status of ON. Motor severity showed a trend that increases with increasing staging, but was not seen when compared to the onset. This result might be affected by the ON/OFF status during examination. Conclusion: Parkinson's disease in Sanglah and Wangaya General Hospital is more common in men and over the age of 50 years, and most are found in moderate severity. There is a trend of worsening motor severity with the increasing Hoehn-Yahr stadium. Examination using UPDRS-III is recommended to be done both on ON and OFF state to get more sensitive results

scholarly journals The BLENDS Method for Data Augmentation of 4-Dimensional Brain Images

2021 ◽  
Author(s):  
Kevin P Nguyen ◽  
Vyom Raval ◽  
Abu Minhajuddin ◽  
Thomas Carmody ◽  
Madhukar H Trivedi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Data Augmentation ◽  
Response Prediction ◽  
Anatomical Variations ◽  
Resting State Fmri ◽  
Training Data ◽  
Learning Models ◽  
Antidepressant Response

Purpose: Data augmentation improves the accuracy of deep learning models when training data is scarce by synthesizing additional samples. This work addresses the lack of validated augmentation methods specific for synthesizing anatomically realistic 4D (3D+time) images for neuroimaging, such as fMRI, by proposing a new augmentation method. Materials and Methods: The proposed method, BLENDS, generates new nonlinear warp fields by combining intersubject coregistration maps, computed using symmetric normalization, through spatial blending. These new warp fields can be applied to existing 4D fMRI to create new augmented images. BLENDS is tested on two neuroimaging problems using de-identified datasets: 1) the prediction of antidepressant response from task-based fMRI in the EMBARC dataset (n = 163), and 2) the prediction of Parkinson's Disease symptom trajectory from baseline resting-state fMRI regional homogeneity in the PPMI dataset (n = 43). Results: BLENDS readily generates hundreds of new fMRI from existing images, with unique anatomical variations from the source images, that significantly improve prediction performance. For antidepressant response prediction, augmenting each original image once (2x the original training data) significantly increased prediction r2 from 0.055 to 0.098 (p < 1e-6), while at 10x augmentation R2 increased to 0.103. For the prediction of Parkinson's Disease trajectory, 10x augmentation R2 increased from 0.294 to 0.548 (p < 1e-6). Conclusion: Augmentation of fMRI through nonlinear transformations with BLENDS significantly improves the performance of deep learning models on clinically relevant predictive tasks. This method will help neuroimaging researchers overcome dataset size limitations and achieve more accurate predictive models.

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Visual Effects of Deep Brain Stimulation in a Patient with Parkinson’s Disease

2019 ◽  
pp. 158-173
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Visual System ◽  
Brain Stimulation ◽  
Neurodegenerative Disorder ◽  
Initial Examination ◽  
Before And After ◽  
Deep Brain ◽  
Cover Test

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.

Peripheral Immunity, Immunoaging and Neuroinflammation in Parkinson’s Disease

2019 ◽  
Vol 26 (20) ◽  
pp. 3719-3753 ◽  
Author(s):  
Natasa Kustrimovic ◽  
Franca Marino ◽  
Marco Cosentino
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Neurodegenerative Disorder ◽  
Neurodegenerative Process ◽  
Progressive Degeneration ◽  
Cytokine Levels ◽  
Inflammatory Phenotype ◽  
Immune Challenges

:Parkinson’s disease (PD) is the second most common neurodegenerative disorder among elderly population, characterized by the progressive degeneration of dopaminergic neurons in the midbrain. To date, exact cause remains unknown and the mechanism of neurons death uncertain. It is typically considered as a disease of central nervous system (CNS). Nevertheless, numerous evidence has been accumulated in several past years testifying undoubtedly about the principal role of neuroinflammation in progression of PD. Neuroinflammation is mainly associated with presence of activated microglia in brain and elevated levels of cytokine levels in CNS. Nevertheless, active participation of immune system as well has been noted, such as, elevated levels of cytokine levels in blood, the presence of auto antibodies, and the infiltration of T cell in CNS. Moreover, infiltration and reactivation of those T cells could exacerbate neuroinflammation to greater neurotoxic levels. Hence, peripheral inflammation is able to prime microglia into pro-inflammatory phenotype, which can trigger stronger response in CNS further perpetuating the on-going neurodegenerative process.:In the present review, the interplay between neuroinflammation and the peripheral immune response in the pathobiology of PD will be discussed. First of all, an overview of regulation of microglial activation and neuroinflammation is summarized and discussed. Afterwards, we try to collectively analyze changes that occurs in peripheral immune system of PD patients, suggesting that these peripheral immune challenges can exacerbate the process of neuroinflammation and hence the symptoms of the disease. In the end, we summarize some of proposed immunotherapies for treatment of PD.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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