Analysis of Parkinson\u27s Disease Data

In this paper, we investigate the diagnostic data from patients suffering with Parkinson\u27s disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson\u27s research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson\u27s Disease diagnosis to certain extent. We use Parkinson\u27s Progression Markers Initiative (PPMI) dataset provided by Michael J. Fox Foundation to perform our analysis

A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases.Comment: 20 pages, 6 tables, 3 figure

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

파킨슨병에서 시상하핵 뇌심부자극술의 미세전극기록으로부터 딥러닝을 이용한 임상 결과 예측

학위논문(석사)--서울대학교 대학원 :의과대학 의학과,2019. 8. 백선하.(Objectives) Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment to improve the motor symptoms of advanced Parkinson disease (PD). Accurate positioning of the stimulation electrodes to STN is mandatory for better clinical outcomes. However, the precise identification of the STN during the microelectrode recording (MER) is not easy. In this study, we analyzed deep learning based MER signals to better predict the clinical outcome of motor function improvement after bilateral STN DBS in patients with advanced PD. (Methods) 696 left MER segments of 4 seconds length from 34 PD patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included in this study. The datasets of thirty patients were assigned to the training set, and the datasets of four patients were assigned to the test set. The wavelet transformed MER and the ratio of DBS on and off Unified Parkinson's Disease Rating Scale(UPDRS) Part III score of the off-medication state were applied for deep learning. According to the ratio, the patients were divided into two groups, high-responder and moderate-responder group. Visual Geometry Group(VGG)-16 model with multi-task learning algorithm was used to estimate the bilateral effect of DBS. To apply the effect of the contralateral score more than ipsilateral score, the ratio of the loss function was varied. Gradient class activation map was used to marking the lesion of interest of CNN. (Results) When we divided MER according to the frequency band and transformed to wavelets, the maximal accuracy was the highest in the 50-500 Hz group, compared with 1-50 Hz and 500-5,000Hz groups. In addition, when the multitask-learning method was applied to 50-500Hz group, the stability of the model was prominently improved. The max accuracy was the highest(80.2%) when the loss ratio of right to left was given as 5:1 or 6:1 in the model. Area under the curve(AUC) was 0.88 in the receiver-operating characteristic(ROC) curve. Gradient class activation map showed that 80-200Hz band was the most commonly referenced area. (Conclusion) We confirmed that the clinical improvement of PD patients who underwent bilateral STN DBS could be predicted based on multi-task deep learning based MER analysis. The deep learning based MER analysis could be helpful for determining the position of the electrode, by predicting motor function improvement.연구 배경 시상하핵의 뇌심부자극술은 진행된 파킨슨병에서 운동 증상을 호전시키는 효과적인 치료이다. 좋은 임상적인 결과를 위해 자극 전극을 정확하게 위치시키는 것이 필요하다. 하지만 미세전극측정을 통해서도 시상하핵을 정확하게 식별하는 것이 쉽지 않다. 이 연구에서는 진행된 파킨슨병 환자에서 딥러닝을 기반으로 미세전극측정을 분석하여 양측 시상하핵 뇌심부자극술 후의 운동기능 호전 정도를 예측하였다. 연구 방법 이 연구에는 전신마취 하에서 양측 시상하핵 뇌심부자극술을 시행받은 34명의 환자로부터 측정된 4초 길이의 좌측 미세전극측정 분절이 포함되었다. 30명의 환자는 훈련군으로 4명의 환자는 실험군으로 구분하였다. 웨이브릿(wavelet) 변환된 미세전극측정 자료와 UPDRS(Unified Parkinson's Disease Rating Scale) 파트 III 중 오프-약물(Off-medication) 시기의 뇌심부자극/비자극 점수가 딥러닝에 사용되었다. 그 비율에 따라 고반응군과 중반응군으로 분류하였다. 다중작업학습 알고리즘을 이용한 VGG-16 모델이 DBS의 양측성 효과를 추정하기 위해 사용되었다. 동측의 점수보다 반대측의 점수를 크게 반영하도록 하기 위해 손실함수(loss function)의 비율을 다양하게 적용 하였다. CNN이 참조한 영역을 표시하기 위해 Grad-CAM을 사용하였다. 연구 결과 미세전극측정신호를 주파수 대역 별로 나누어 웨이브릿 변환하였을 때, 최대정확도는 1-50Hz와 500-5,000Hz와 비교하여 50-500Hz에서 가장 높았다. 게다가 다중작업학습을 적용하였을 때 모델의 안정도가 더 개선되었다. 최대 정확도는 좌우 손실함수의 비율이 5:1과 6:1 때 80.2%로 가장 높았다. 수신자 조작 특성 곡선(ROC curve)에서 곡선하 면적(AUC) 값은 0.88이었다. Grad-CAM에서는 80-200Hz 대역을 가장 흔히 참조한 것을 보여주었다. 연구 결론 미세전극측정의 다중작업학습을 통한 분석으로 파킨슨병 환자에서 양측 시상하핵 뇌심부자극술 시행 후 임상적 호전에 관한 예측이 가능할 것으로 판단하였다. 딥러닝으로 미세전극측정신호를 분석하여 수술 후 운동기능향상을 예측함으로써, 전극의 위치를 결정하는 데에 도움이 될 것으로 기대한다.Introduction 5 PD - DBS - UPDRS 5 Signal - CNN - Clinical outcome 5 Methods 7 Subjects 7 Surgical procedure 8 Microelectrode Recordings 9 Wavelet Transformation 9 Training set and Test set 10 Deep learning 11 Multi-task learning 12 Gradient class activation map 13 Statistical analysis 14 IRB 14 Results 15 Patient Data 15 MER & clinical outcome relation 21 Gradient Class Activation Map 24 Discussion 26 Single-task Learning 26 Multi-task Learning 27 Gradient Class Activation Map 28 Expected Clinical Usefulness 28 Limitation 29 Conclusion 31 References 32Maste

Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III

BACKGROUND: Parkinson's disease severity is typically measured using the Movement Disorder Society Unified Parkinson's disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. OBJECTIVE: In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. METHODS: Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. RESULTS: KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. CONCLUSION: We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation

Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method

Background and Objective: Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's, characterized by motor and non-motor symptoms. Developing a method to diagnose the condition in its beginning phases is essential because of the significant number of individuals afflicting with this illness. PD is typically identified using motor symptoms or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are expensive, time-consuming, and unavailable to the general public; furthermore, they are not very accurate. These constraints encouraged us to develop a novel technique using SHAP and Hard Voting Ensemble Method based on voice signals. Methods: In this article, we used Pearson Correlation Coefficients to understand the relationship between input features and the output, and finally, input features with high correlation were selected. These selected features were classified by the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Gradient Boosting, and Bagging. Moreover, the Hard Voting Ensemble Method was determined based on the performance of the four classifiers. At the final stage, we proposed Shapley Additive exPlanations (SHAP) to rank the features according to their significance in diagnosing Parkinson's disease. Results and Conclusion: The proposed method achieved 85.42% accuracy, 84.94% F1-score, 86.77% precision, 87.62% specificity, and 83.20% sensitivity. The study's findings demonstrated that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases