Diagnosis Of Parkinson's Disease Research Articles

Blood test for early Parkinson disease diagnosis.

Blood test for early Parkinson disease diagnosis.

Generation of induced pluripotent stem cells (NIMHi015-A) from a Parkinson’s Disease patient harbouring a homozygous Exon 3 deletion in the PRKN gene

The Parkin (PRKN) gene mutation is prevalent in young-onset Parkinson's disease (PD), typically emerging before age 30, accompanied by early motor symptoms. Induced pluripotent stem cells (iPSCs) were derived from peripheral blood mononuclear cells of a PD patient with an exon 3 deletion in PRKN using Sendai-virus reprogramming. PD diagnosis was confirmed via the Unified Parkinson's Disease Rating Scale (UPDRS). Characterization of the iPSC line ensured self-renewal and pluripotency. This resource serves as a valuable platform for drug screening and elucidating the pathophysiology of this mutation, facilitating advancements in PD research.

Identifying Barriers and Facilitators to Accessing Care for Historically Marginalized Communities Affected by Parkinson Disease: A Qualitative Study.

Parkinson disease (PD) is the second most common neurodegenerative disease. Members of the Black Diaspora (MBD) and Hispanic/Latinx people are less likely to receive a timely diagnosis following the onset of symptoms and more likely to experience greater disease severity due to late diagnosis. Historically marginalized populations (i.e., MBD, Hispanic, and Latinx communities) are not accurately represented in research; this, along with many other barriers, compounds underreporting and lack of recognition of PD. It is important to understand barriers to early diagnosis and healthcare access for these historically marginalized populations from the community's perspective. Our team conducted two focus groups to identify barriers and facilitators to PD healthcare-seeking behavior. We sought to identify which barriers are modifiable to ultimately improve engagement in neurological care for MBD and Hispanic individuals affected by PD. We enrolled 15 participants (13 female; African/African American/Black n = 10, Hispanic/Puerto Rican n = 3, other n = 2) for two focus groups. Discussions revealed sources of barriers to healthcare-seeking behavior in three main domains: legacy of racism in the United States, ancestral cultural environment, and healthcare system access. These sources influenced individuals' PD knowledge and familiarity. Additionally, participants expressed a desire to know more about PD and called for increased community-based programming for education and awareness. This paper uses a community-based participatory research approach to describe the experiences of MBD, Hispanic, and Latinx people in Manhattan and the surrounding areas in relation to possible sources of healthcare disparities and delayed PD diagnosis. These sources have broad implications and should be addressed through collaborative community programming.

Disease burden of Parkinson's disease in China and its provinces from 1990 to 2021: findings from the global burden of disease study 2021

Parkinson's disease (PD) has become a public health concern with global ageing. However, comprehensive assessments of the temporal and geographical trend of PD disease burden in China remain insufficient. This study aimed to examine the burden of PD by age, gender, and geographical region in China during 1990-2021. Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we analysed the incidence, prevalence, mortality, and DALY burden of PD in 33 Chinese provinces/regions. We compared the national figure with the global average and the corresponding estimates from the G20 countries. The estimated annual percentage change (EAPC) was used to quantify the temporal trends of PD burden during 1990-2021. We further assessed the PD burden by age and gender during 1990-2021. We used a decomposition analysis to investigate the changes in the number of new cases, patients, and deaths of PD during 1990-2021. In 2021, China recorded the highest age-standardised incidence and prevalence of PD among the G20 countries, at 24.3 per 100,000 and 245.7 per 100,000, respectively, figures that were much higher than the global average. During 1990-2021, the age-standardised incidence of PD in China increased by 89.7%, and the age-standardised prevalence by 167.8%, both marking the largest increases among the G20 countries. In contrast, the age-standardised mortality for PD has significantly decreased since 1990, whereas the age-standardised DALY rate for PD has remained relatively unchanged since 1990. The PD burden gradually increased with age, especially in the elderly population aged ≥65 years. During 1990-2021, the burden in males consistently surpassed that in females, with the gender difference widening over time. The increase in new cases and patients of PD was primarily driven by changes in age-specific rates, while the rise in PD-related deaths was largely attributable to population ageing. The disease burden of PD varied considerably across the Chinese provinces. In 2021, age-standardised incidence and prevalence of PD were generally higher in China's southeastern coastal regions than in the western regions, and age-standardised DALY rates were higher in the northern regions than in other regions. The disease burden of PD in China has consistently risen over the past three decades, particularly among elderly men. The increasing causative factors and population aging highlight the need for enhancing public health intervention and resource allocation, especially in etiological research, early diagnosis, preventive strategies, and region-specific management for PD. Ministry of Science and Technology of the People's Republic of China (2022YFC2304900, 2022YFC2505100); National Key R&D Program of China (2022YFC2505100, 2022YFC2505103, 2018YFC1315300); Outstanding Young Scholars Support Program (grant number: 3111500001); Epidemiology modeling and risk assessment (grant number: 20200344), and Xi'an Jiaotong University Young Scholar Support Grant (grant number: YX6J004).

Clinical value of video oculomotor evaluation in the differential diagnosis of multiple system atrophy and Parkinson's disease.

Multiple system atrophy (MSA) is a neurodegenerative disease that progresses rapidly and has a poor prognosis. This study aimed to assess the value of video oculomotor evaluation (VOE) in the differential diagnosis of MSA and Parkinson's disease (PD). In total, 28 patients with MSA, 31 patients with PD, and 30 age- and sex-matched healthy controls (HC) were screened and included in this study. The evaluation consisted of a gaze-holding test, smooth pursuit eye movement (SPEM), random saccade, and optokinetic nystagmus (OKN). The MSA and PD groups had more abnormalities and decreased SPEM gain than the HC group (64.29%, 35.48%, 10%, p<.001). The SPEM gain in the MSA group was significantly lower than that in the PD group at specific frequencies. Patients with MSA and PD showed prolonged latencies in all saccade directions compared with those with HC. However, the two diseases had no significant differences in the saccade parameters. The OKN gain gradually decreased from the HC to the PD and the MSA groups (p<.05). Compared with the PD group, the gain in the MSA group was further decreased in the OKN test at 30°/s (Left, p=.010; Right p=.016). Receiver operating characteristic curves showed that the combination of oculomotor parameters with age and course of disease could aid in the differential diagnosis of patients with MSA and PD, with a sensitivity of 89.29% and a specificity of 70.97%. The combination of oculomotor parameters and clinical data may aid in the differential diagnosis of MSA and PD. Furthermore, VOE is vital in the identification of neurodegenerative diseases.

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Associations of street-view greenspace with Parkinson’s disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

IntroductionProtective associations of greenspace with Parkinson’s disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). MethodsWe created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. ResultsThere were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. ConclusionIncreasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning

Introduction/ Background This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease. Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection. Materials and Methods We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics. Results The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis. Discussion This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare. Conclusion The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients.

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Improving Medication Management for Inpatients with a Secondary Diagnosis of Parkinson Disease.

Patients who have Parkinson disease require individualized medication regimens to optimize care. A review of the medication management of patients admitted to a tertiary care hospital with a secondary diagnosis of Parkinson disease found significant departures from the patients' home regimen. Medication regimens are often altered by health care teams unfamiliar with Parkinson disease-specific care in order to conform to standard hospital medication orders and administration times, potentially resulting in increased patient falls, delirium, and mortality.A nurse-led multidisciplinary team consisting of pharmacy, nursing, informatics, neurology, and quality personnel implemented a quality improvement (QI) project between July 2020 and July 2022 to identify patients with Parkinson disease, including those with a secondary diagnosis and those undergoing deep brain stimulation, and customize medication management in order to reduce length of stay, mortality, falls, falls with harm, and 30-day readmissions. The QI project team also evaluated patient satisfaction with medication management.Among patients with a secondary diagnosis of Parkinson disease, the proportion who had medication histories conducted by a pharmacy staff member increased from a baseline of 53% to more than 75% per month. For all patients with Parkinson disease, those whose medication history was taken by a pharmacy staff member had orders matching their home regimen 89% of the time, whereas those who did not had orders matching the home regimen only 40% of the time. Among patients with a secondary diagnosis of Parkinson disease, the length-of-stay index decreased from a baseline of 1 to 0.94 and observed-to-expected mortality decreased from 1.03 to 0.78. The proportion of patients experiencing a fall decreased from an average of 5% to 4.08% per quarter, while the proportion of patients experiencing a fall with harm decreased from an average of 1% to 0.75% per quarter. The rate of 30-day readmissions decreased from 10.81% to 4.53% per quarter. Patient satisfaction scores were 1.95 points higher for patients who had medication histories taken by pharmacy than for those who did not (5 versus 3.05).

Association of Misfolded α-Synuclein Derived from Neuronal Exosomes in Blood with Parkinson's Disease Diagnosis and Duration.

Misfolded α-synuclein can be detected in blood samples of Parkinson's disease (PD) patients by a seed amplification assay (SAA), but the association with disease duration is not clear, yet. In the present study we aimed to elucidate whether seeding activity of misfolded α-synuclein derived from neuronal exosomes in blood is associated with PD diagnosis and disease duration. Cross-sectional samples of PD patients were analyzed and compared to samples of age- and gender-matched healthy controls using a blood-based SAA. Presence of α-synuclein seeding activity and differences in seeding parameters, including fluorescence response (in arbitrary units) at the end of the amplification assay (F60) were analyzed. Additionally, available PD samples collected longitudinally over 5-9 years were included. In the cross-sectional dataset, 79 of 80 PD patients (mean age 69 years, SD = 8; 56% male) and none of the healthy controls (n = 20, mean age 70 years, SD = 10; 55% male) showed seeding activity (sensitivity 98.8%). When comparing subgroups divided by disease duration, longer disease duration was associated with lower α-synuclein seeding activity (F60: p <  0.001). In the longitudinal analysis 10/11 patients showed a gradual decrease of α-synuclein seeding activity over time. This study confirms the high sensitivity of the blood-based α-synuclein SAA applied here. The negative association of α-synuclein seeding activity in blood with disease duration makes this parameter potentially interesting as biomarker for future studies on the pathophysiology of disease progression in PD, and for biologically oriented trials in this field.

Detecting Misfolded α-Synuclein in Blood Years before the Diagnosis of Parkinson's Disease.

Identifying individuals with Parkinson's disease (PD) already in the prodromal phase of the disease has become a priority objective for opening a window for early disease-modifying therapies. The aim was to evaluate a blood-based α-synuclein seed amplification assay (α-syn SAA) as a novel biomarker for diagnosing PD in the prodromal phase. In the TREND study (University of Tuebingen) biennial blood samples of n = 1201 individuals with/without increased risk for PD were taken prospectively over 4 to 10 years. We retrospectively analyzed blood samples of 12 participants later diagnosed with PD during the study to detect and amplify pathological α-syn conformers derived from neuronal extracellular vesicles using (1) immunoblot analyses with an antibody against these conformers and (2) an α-syn-SAA. Additionally, blood samples of n = 13 healthy individuals from the TREND cohort and n = 20 individuals with isolated rapid eye movement sleep behavior disorder (iRBD) from the University Hospital Cologne were analyzed. All individuals with PD showed positive immunoblots and a positive α-syn SAA at the time of diagnosis. Moreover, all PD patients showed a positive α-syn SAA 1 to 10 years before clinical diagnosis. In the iRBD cohort, 30% showed a positive α-syn SAA. All healthy controls had a negative SAA. We here demonstrate the possibility to detect and amplify pathological α-syn conformers in peripheral blood up to 10 years before the clinical diagnosis of PD in individuals with and without iRBD. The findings of this study indicate that this blood-based α-syn SAA assay has the potential to serve as a diagnostic biomarker for prodromal PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Combined diagnosis for Parkinson's disease via gait and eye movement disorders

Background and objectivesWith the discovery of the potential role of gait and eye movement disorders in Parkinson's disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD. MethodsWe enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages. ResultsA total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients. ConclusionWe demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.

Efficacy of antiparkinsonian therapy in tremor correction according to tremorography

Objective: to evaluate the efficacy of antiparkinsonian drugs of different groups considering the frequency-amplitude characteristics in the correction of tremulous hyperkinesis in Parkinson's disease (PD) using tremorography. Material and methods. 172 patients with a confirmed diagnosis of PD were observed. Taking into account age and contraindications, patients were divided into four groups receiving one of four drugs in monotherapy: dopamine receptor agonists (ADR; pramipexole), levodopa (Tidomet forte), anticholinergics (biperiden), amantadines (amantadine sulfate). Before starting treatment and after completion of the selected therapy, patients underwent tremorography to analyse the frequency, amplitude and duration of tremor over time in four standard tests. Results. All antiparkinsonian drugs reduced rest tremor, and the efficacy of pramipexole in treating tremor was comparable to that of levodopa in the early stages of the disease. As the severity of the disease increased, the efficacy of ADRs decreased, whereas levodopa became more important. Amantadine sulfate showed the lowest efficacy in rest tremor. The efficacy of anticholinergics against tremor is high but inconsistent. Conclusion. We demonstrated feasibility of using tremorography in the selection of an individualised treatment programme for patients with shaking hyperkinesis in PD , and the high efficacy of levodopa in shaking hyperkinesis was confirmed.

Elevated Serum Xanthine Oxidase and Its Correlation with Antioxidant Status in Patients with Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative movement disorder associated with a loss of dopamine neurons in the substantia nigra. The diagnosis of PD is sensitive since it shows clinical features that are common with other neurodegenerative diseases. In addition, most symptoms arise at the late stage of the disease, where most dopaminergic neurons are already damaged. Several studies reported that oxidative stress is a key modulator in the development of PD. This condition occurs due to excess reactive oxygen species (ROS) production in the cellular system and the incapability of antioxidants to neutralize it. In this study, we focused on the pathology of PD by measuring serum xanthine oxidase (XO) activity, which is an enzyme that generates ROS. Interestingly, the serum XO activity of patients with PD was markedly upregulated compared to patients with other neurological diseases (ONDs) as a control. Moreover, serum XO activity in patients with PD showed a significant correlation with the disease severity based on the Hoehn and Yahr (HY) stages. The investigation of antioxidant status also revealed that serum uric acid levels were significantly lower in the severe group (HY ≥ 3) than in the ONDs group. Together, these results suggest that XO activity may contribute to the development of PD and might potentially be a biomarker for determining disease severity in patients with PD.

Transferrin-conjugated magnetic nanoparticles for the isolation of brain-derived blood exosomal MicroRNAs: A novel approach for Parkinson's disease diagnosis

BackgroundBrain-derived exosomes circulate in the bloodstream and other bodily fluids, serving as potential indicators of neurological disease progression. These exosomes present a promising avenue for the early and precise diagnosis of neurodegenerative conditions. Notably, miRNAs found in plasma extracellular vesicles (EVs) offer distinct diagnostic benefits due to their stability, abundance, and resistance to breakdown. ResultsIn this study, we introduce a method using transferrin conjugated magnetic nanoparticles (TMNs) to isolate these exosomes from the plasma of patients with neurological disorders. This TMNs technique is both quick (<35 min) and cost-effective, requiring no high-priced ingredients or elaborate equipment for EV extraction. Our method successfully isolated EVs from 33 human plasma samples, including those from patients with Parkinson's disease (PD), Multiple Sclerosis (MS), and Dementia. Using quantitative polymerase chain reaction (PCR) analysis, we evaluated the potential of 8 exosomal miRNA profiles as biomarker candidates. Six exosomal miRNA biomarkers (miR-195-5p, miR-495-3p, miR-23b-3P, miR-30c-2-3p, miR-323a-3p, and miR-27a-3p) were consistently linked with all stages of PD. SignificanceThe TMNs method provides a practical, cost-efficient way to isolate EVs from biological samples, paving the way for non-invasive neurological diagnoses. Furthermore, the identified miRNA biomarkers in these exosomes may emerge as innovative tools for precise diagnosis in neurological disorders including PD.

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

Melanoma Risk and Timing of Parkinson Disease Diagnosis (P4-3.004)

Melanoma Risk and Timing of Parkinson Disease Diagnosis (P4-3.004)