Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

INTRODUCTION: Parkinson's Disease (PD) is a progressive neurological disorder affecting a significant portion of the global population, leading to profound impacts on daily life and imposing substantial burdens on healthcare systems. Early identification and precise classification are crucial for effectively managing this disease. This research investigates the potential of deep learning techniques in facilitating early recognition and accurate classification of PD. OBJECTIVES: The primary objective of this study is to leverage advanced deep learning techniques for the early detection and precise classification of Parkinson's Disease. By utilizing a rich dataset comprising speech signal features extracted from 3000 PD patients, including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features, and TWQT features, this research aims to evaluate the performance of various deep learning models in PD classification. METHODS: The dataset containing diverse speech signal features from PD patients' recordings serves as the foundation for training and evaluating five different deep learning models: ResNet50, VGG16, Inception v2, AlexNet, and VGG19. Each model undergoes training and assessment to determine its capability in accurately classifying PD patients. Performance metrics such as accuracy are employed to evaluate the models' effectiveness. RESULTS: The results demonstrate promising potential, with overall accuracies ranging from 89% to 95% across the different deep learning models. Notably, AlexNet emerges as the top-performing model, achieving an accuracy of 95% and demonstrating balanced performance in accurately identifying both true and false PD cases. CONCLUSION: This research highlights the significant potential of deep learning in facilitating the early detection and classification of Parkinson's Disease. Leveraging speech signal features offers a non-invasive and cost-effective approach to PD assessment. The findings contribute to the growing body of evidence supporting the integration of artificial intelligence in healthcare, particularly in the realm of neurodegenerative disorders. Further exploration into the application of deep learning in this domain holds promise for advancing PD diagnosis and management.

Parkinson's Disease (PD) is a neurodegenerative condition that presents considerable challenges in achieving accurate early diagnosis and classification. This research explores the classification of PD through the application of advanced deep learning techniques integrated with optimization methods, compared to conventional machine learning approaches. The analysis is conducted using a diverse dataset incorporating clinical, vocal, and movement-related features to ensure comprehensive evaluation. Deep learning frameworks, such as multi-layer perceptron (MLP), Long Short-Term Memory (LSDM), the proposed deep learning model namely CNN-BiGRU were enhanced using strategies like hyperparameter optimization, regularization, and advanced gradient-based optimizers to boost performance and minimize overfitting. Similarly, traditional machine learning models, including Linear Regression, Random Tree, REP Tree, and Random Forest, were implemented and tested on the same dataset. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the curve (AUC), were used to measure and compare the performance of all models. The findings reveal that optimized deep learning models significantly surpass traditional machine learning methods in both classification accuracy and generalization. This study emphasizes the effectiveness of optimization-enhanced deep learning techniques in PD classification and their clear advantages over traditional models.

Parkinson's disease (PD) classification plays a crucial role in medical diagnosis and patient management. Identifying Parkinson's disease at an early stage can lead to more effective treatment and improved patient outcomes. However, existing methods for Parkinson's disease classification face several limitations. The foremost limitation is the need for accurate and reliable diagnostic tools, as misdiagnosis can lead to inappropriate treatments and unnecessary stress for patients. Thus, a hybrid deep learning model is introduced in this research. The proposed model involves the utilization of EEG signals obtained from a publicly available dataset. Key features are extracted from the EEG signals using a bandpass filter, and every feature is associated with specific brainwave frequencies and cognitive states. The feature mapping and classification are executed through the Chaotic Chebyshev Zebra optimization- based Residual GhostNet (CCZO_Residual_GhostNet). This hybrid classifier, Residual GhostNet, combines ResNet-152 with GhostNet, enhancing classification precision. Furthermore, the CCZO algorithm optimizes the loss function, introducing elements of chaos and Chebyshev mapping to improve classification accuracy. The assessment based on accuracy, sensitivity, specificity, and F-score acquired 98.76%, 98.59%, 98.95%, and 99%, respectively.

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in substantia nigra, resulting in both motor impairments and cognitive decline. Traditional PD classification methods are expert-dependent and time-intensive, while existing deep learning (DL) models often suffer from inconsistent accuracy, limited interpretability, and inability to fully capture PD's clinical heterogeneity. This study proposes a novel framework Enhanced EfficientNet-Extended Multimodal PD Classification with Hybrid Particle Swarm and Grey Wolf Optimizer (EEFN-XM-PDC-HybPS-GWO) to overcome these challenges. The model integrates T1-weighted MRI, DaTscan images, and gait scores from NTUA and PhysioNet repository respectively. Denoising is achieved via Multiscale Attention Variational Autoencoders (MSA-VAE), and critical regions are segmented using Semantic Invariant Multi-View Clustering (SIMVC). The Enhanced EfficientNet-Extended Multimodal (EEFN-XM) model extracts and fuses image and gait features, while HybPS-GWO optimizes classification weights. The system classifies subjects into early-stage PD, advanced-stage PD, and healthy controls (HCs). Ablation analysis confirms the hybrid optimizer's contribution to performance gains. The proposed model achieved 99.2% accuracy with stratified 5-fold cross-validation, outperforming DMFEN-PDC, MMT-CA-PDC, and LSTM-PDD-GS by 7.3%, 15.97%, and 10.43%, respectively, and reduced execution time by 33.33%. EEFN-XM-PDC-HybPS-GWO demonstrates superior accuracy, computational efficiency, and clinical relevance, particularly in early-stage diagnosis and PD classification.

Explainable hypergraphs for gait based Parkinson classification

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pretrained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.

An optimized framework for Parkinson's disease classification using multimodal neuroimaging data with ensemble-based and data fusion networks.

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson's dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

Diagnosis is the key step forward to cure a disease. Deep learning is becoming popular as a tool for usage in medical diagnosis. The existing literature using deep learning for the diagnosis of Parkinson’s disease (PD) by transfer learning of MRI data was limited to the AlexNet architecture. The present work aims to inculcate commonly used deep learning architectures using transfer learning for effective diagnosis of PD using MRI data. The best three performing models are selected based on the standard metric called F1-score. An ensemble model is proposed based on the maximum probability across all the selected models for PD classification. The approach mainly focuses on the effective diagnosis of PD. The performance of the proposed ensemble approach is validated using the standard metrics known as F1-score and classification accuracy. Among the commonly used deep learning architectures, it was found that VGG19 is better than the existing state-of-the-art, which is AlexNet. The proposed ensemble approach applied on top three commonly used deep learning models led to the improvement in accuracy to 0.978. The false positive (FP) and false negative (FN) were reduced significantly by the proposed ensemble approach.KeywordsDeep learningTransfer learningEnsemble methodParkinson’s diseaseCNN

Parkinson’s disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30–60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use.Clinical Trial Registration[09/H0906/82].

Parkinson's Disease is a neurological condition that affects the brain. It causes tremors in the body, hands, and spine, and causes the body to stiffen. A cure or treatment is not yet available, even though the condition is quite advanced. It is only possible to treat a disease when it is in its early stages or onset. As well as cutting the disease's costs, these measures could also save a person's life. In this paper, the study will be on Parkinson's Disease and the capsule network will be used for classification of the Parkinson's Disease. Dataset used for the analysis is downloaded from the Physionet, which consists of images. Research presented here focuses on applying deep learning models to fully understand Parkinson's Disease and identify its earliest signs. In order to assess the model, we look at its precision, recall, and other related metrics. The result section has shown that the capsule network has performed better than the other existing algorithms.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

ObjectiveThis work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.Material and MethodsA large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson’s disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models’ decision.ResultsA comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders.DiscussionOur results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods.ConclusionThe ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database “Image and Data Archive (IDA)” is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.