Early Detection Of Parkinson's Disease Research Articles

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.

Correlations among serum alpha-(1,6)-fucosyltransferase and early symptoms associated with Parkinson's disease: A cross-sectional retrospective study

Alpha-(1,6)-fucosyltransferase (FUT8) has been found to play a role in modulating the central immune system and inflammatory responses. Limited studies have assessed the correlations between serum FUT8 levels and various non-motor symptoms associated with early Parkinson's disease (PD). Therefore, our research aims to investigate the associations between serum FUT8 levels and symptoms such as smell dysfunction, sleep duration, sleep problems, and MMSE scores in PD patients. FUT8 and neurofilament light chain (NfL) levels were measured using enzyme-linked immunosorbent assays (ELISA). We analyzed the correlations between serum FUT8 levels, NfL, and early symptoms of PD using Spearman's correlation, multiple linear regression, and logistic regression models. The expression of FUT8 in CSF samples from PD patients was significantly upregulated, with its protein levels in CSF being positively associated with serum levels. Furthermore, there were significant positive associations between serum FUT8 levels with NfL levels, smell dysfunction, short sleep duration, and long sleep duration. However, a significant inverse relationship was observed between FUT8 levels and MMSE scores. Additionally, we explored gender and age differences in the correlations of FUT8 levels and early symptoms in patients. This study reveals that increased FUT8 levels are positively correlated with a higher risk of early PD-associated symptoms. These findings suggest that serum FUT8 could serve as a promising biomarker for the early detection of PD.

Early Detection of Parkinson’s Disease Using Machine Learning

A hallmark of Parkinson's disease is the degeneration of dopaminergic neurons in the midbrain's substantia nigra pars compacta. However, machine learning is needed to design and implement early Parkinson's disease detection. by using machine learning methods such as CNN and SVM, which can reliably identify voice signals and spiral images to identify early indications of Parkinson's disease. Using machine learning on handwriting, tremor, and gait datasets, the method addresses the shortcomings of individual analyses for a more complete diagnosis solution by investigating relationships between symptoms. This increases accuracy. When voice and spiral drawing data were combined, Parkinson's disease diagnosis accuracy showed promise, with the machine learning model successfully differentiating between unaffected patients and those who were affected. This observation suggests a viable path for precise Parkinson's disease diagnosis: an integrated method that combines machine learning techniques with data from spiral drawings and voice.

Dynamic Prediction of Excessive Daytime Sleepiness Through Random Survival Forest: An application of the PPMI data

Parkinson disease (PD) is the second most widespread neurodegenerative disease worldwide. Excessive daytime sleepiness (EDS) has a significant correlation in de novo PD patients. Identifying predictors is critical in order for early detection of disease diagnosis. We investigated clinical and biological markers related with time-dependent variables in sleepiness for early detection of PD. Data were obtained from the Parkinson’s Progression Markers Initiative study, which evaluates the progression markers in patients. The dataset also includes various longitudinal endogenous predictors. The measures of EDS were obtained through the Epworth Sleepiness Scale (ESS). Random survival forest method which can be deal with multivariate longitudinal endogenous predictors was used to predict the probability of having EDS in PD. The rate of having EDS among PD disease was 0.452. The OOB rate was 0.186. The VIMP and minimal depth indicated that the most important variables are stai state, JLO and the presence of ApoE4 Allele. In early PD, EDS is good indicator of the diagnosis of the PD and it increases over time and has association with several predictors.

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition.
 OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment.
 METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4].
 RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. .
 CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

Modified VGG-19 Deep Learning Strategies for Parkinson's Disease Diagnosis - A Comprehensive Review and Novel Approach

Parkinson's Disease (PD) poses a significant risk to clinical experts due to its rapid progression, adversely affecting individuals afflicted by the condition. This neurological disorder manifests through a spectrum of motor and non-motor symptoms, ranging from motor impairments like stiffness and bradykinesia to mental health issues and stress-related illnesses. Early and accurate diagnosis is pivotal for effective treatment, yet traditional diagnostic methods present challenges in detection and management. Leveraging Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), holds promise for enhancing PD diagnosis. This study provides an extensive literature review of recent advancements in DL-based PD diagnosis, encompassing various approaches and developments. Specifically, the proposed modification to the VGG19 model, incorporating additional layers and regularization techniques, demonstrates remarkable performance in distinguishing PD patients from healthy individuals. Experimental results indicate a 98% accuracy rate, underscoring the model's potential as a reliable tool for early PD detection. Moreover, the study evaluates performance metrics, including accuracy, precision, recall, and F1-score, across different DL models, with the modified VGG19 model outperforming alternatives. The findings suggest that DL algorithms, particularly the proposed model, exhibit superior capability in identifying relevant cases and minimizing misclassifications. In conclusion, this research presents a novel DL approach for PD diagnosis using MRI images, offering substantial advancements in accuracy and efficiency. The model's ability to detect PD with high precision non-invasively underscores its significance in facilitating timely treatment and improving patient outcomes. Ultimately, early and accurate diagnosis facilitated by DL methodologies has the potential to revolutionize PD management, marking a significant stride towards enhancing patient care in neurology.

Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention. The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice. Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability. The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08. The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Quantum mayfly optimization based feature subset selection with hybrid CNN for biomedical Parkinson’s disease diagnosis

Parkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum Mayfly Optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson’s disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson’s disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.

Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease

Parkinson’s disease (PD) lacks reliable, non-invasive biomarker tests for early intervention and management. Thus, a minimally invasive test for the early detection and monitoring of PD and REM sleep behavior disorder (iRBD) is a highly unmet need for developing drugs and planning patient care. Extracellular vehicles (EVs) are found in a wide variety of biofluids, including plasma. EV-mediated functional transfer of microRNAs (miRNAs) may be viable candidates as biomarkers for PD and iRBD. Next-generation sequencing (NGS) of EV-derived small RNAs was performed in 60 normal controls, 56 iRBD patients and 53 PD patients to profile small non-coding RNAs (sncRNAs). Moreover, prospective follow-up was performed for these 56 iRBD patients for an average of 3.3 years. Full-scale miRNA profiles of plasma EVs were evaluated by machine-learning methods. After optimizing the library construction method for low RNA inputs (named EVsmall-seq), we built a machine learning algorithm that identified diagnostic miRNA signatures for distinguishing iRBD patients (AUC 0.969) and PD patients (AUC 0.916) from healthy individuals; and PD patients (AUC 0.929) from iRBD patients. We illustrated all the possible expression patterns across healthy-iRBD-PD hierarchy. We also showed 20 examples of miRNAs with consistently increasing or decreasing expression levels from controls to iRBD to PD. In addition, four miRNAs were found to be correlated with iRBD conversion. Distinct characteristics of the miRNA profiles among normal, iRBD and PD samples were discovered, which provides a panel of promising biomarkers for the identification of PD patients and those in the prodromal stage iRBD.

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment.

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19

Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.

Mg-based micromotors for electrochemical detection of Parkinson's disease blood biomarkers

Efficiently capturing and detecting α-synuclein (α-syn) in the bloodstream is crucial for early Parkinson's Disease (PD) diagnosis. In this study, we achieved efficient α-syn detection using electrochemical methods with Mg-based micromotors containing Fe3O4/GOx nanoparticles (NPs) and anti-α-syn antibodies. Leveraging the asymmetrical structure of the micromotors, we significantly improved the movement range of the magnesium (Mg) microsphere within the blood environment. By harnessing its enhanced motility in blood, we achieved efficient capture of α-syn in whole blood. Moreover, we developed an electrochemical detection device capable of detecting captured α-syn and amplifying the current signal through GOx's catalytic activity. This novel strategy offers potential insights for early PD detection and timely treatment.

Enhancing Parkinson’s disease identification using ensemble classifier and data augmentation techniques in machine learning

Parkinson’s disease (PD) is a devastating neurological syndrome that affects millions of people worldwide. For the successful treatment and control of PD, it is essential to detect it early and diagnose it accurately. Machine learning (ML) algorithms have shown promising results in identifying PD based on various clinical and non-invasive measures. This paper proposes an ensemble classifier-based method to identify PD using ML algorithms. We consider two classes of PD, namely, healthy controls and PD patients. Our approach involves the use of feature selection, feature extraction, and classification techniques to develop a robust and accurate model. We use a dataset that includes clinical measures and necessary features from patients with PD and healthy controls. Our outcomes demonstrate the effectiveness of the proposed method in accurately identifying PD and highlight the importance of ML algorithms in assisting with early detection and diagnosis of PD.

High-resolution superlet transform based techniques for Parkinson's disease detection using speech signal

Parkinson's Disease (PD) is a matter of great concern when it comes to the health management of elderly people. Tremors, muscle stiffness, change in cognitive abilities, and dysarthria are some of the frequently encountered symptoms, commonly found in patients diagnosed with PD. A substantive progression of PD can be seen in the patient's body, caused by the degeneration of the dopamine-generating cells. PD is still a non-curable disease but its progression can be slowed down with some medications and therapies. Thus, early detection of the disease becomes a necessity for much-needed care of the patients. Recent developments in the technology sector have rewarded us with an opportunity for early detection of PD using speech signals. In this paper, a Deep Neural Network (DNN) based approach using spectrograms of speech signals generated by Superlet Transform (SLT) is proposed for the detection of PD. The SLT converts the 1-D speech signals into 2-D spectrogram. These spectrograms of the speech signal are then applied to different DNN classifiers namely InceptionResNetV2, VGG-16, and ResNet50v2 for PD detection. The proposed method is evaluated on sustainable vowels, modulated vowels, DDK analysis, and isolated words of PC-GITA dataset and vowels of ItalianPVS dataset. Several evaluation measures have been used to estimate the robustness and explainability of this work. The developed framework gives the best performance on modulated vowels using VGG-16 on PC-GITA dataset. The VGG-16 with SLT achieved an overall accuracy of 92% with sensitivity, specificity, precision, and F-1 score of 92%, 91%, 95%, and 93%, respectively. The proposed method achieved an accuracy of 96% on the ItalianPVS dataset. This method outperforms the state-of-the-art methods like Hilbert spectrum analysis, Empirical Mode Decomposition (EMD), Continuous Wavelet Transform (CWT), and Short-Time Fourier Transform (STFT) for PD detection using different speech data sources.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease.

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson's Disease Using Complex and Large Vocal Features.

Parkinson's disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world's population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject's voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network's potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution's space and time complexity.

Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings.

Parkinson's disease (PD) is the 2nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson's disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance- There is an urgent need to develop non invasive biomarker of Parkinson's disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases' symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

Highly sensitive biosensor based on IGZO thin-film transistors for detection of Parkinson's disease

α-Synuclein (α-Syn) is a major biomarker of Parkinson's disease (PD). Concentration detection of α-Syn in cerebrospinal fluid and plasma of patients are used as a clue for early PD detection. Herein, we propose a thin-film transistor (TFT) biosensor based on an indium gallium zinc oxide (IGZO) thin film fabricated by the sol-gel method. The IGZO TFT biosensor is used for the early detection of PD after surface functionalization for α-Syn. The surface functionalization worked on the surface of the extended gate indium tin oxide sensing platform instead of the channel layer of TFT. Depending on concentrations of α-Syn in human cerebrospinal fluid and plasma, we diluted six different concentrations of α-Syn for detection, ranging from 1 pg ml−1 to 100 ng ml−1. The result shows that the biosensor exhibits high sensitivity and selectivity for α-Syn, a reliable sensing performance with a sensitivity of 189.9 mV dec−1, and a coefficient of determination R2 of 99.7% between 10 pg ml−1 and 100 ng ml−1.