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

KOMPLEKSOWE METODY UCZENIA MASZYNOWEGO I UCZENIA GŁĘBOKIEGO DO KLASYFIKACJI CHOROBY PARKINSONA I OCENY JEJ NASILENIA

In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.W tym badaniu naszym celem było przyjęcie kompleksowego podejścia do kategoryzacji i oceny ciężkości choroby Parkinsona poprzez wykorzystanie technik zarówno uczenia maszynowego, jak i głębokiego uczenia. Dokładnie oceniliśmy skuteczność różnych modeli, w tym XGBoost, Random Forest, Multi-Layer Perceptron (MLP) i Recurrent Neural Network (RNN), wykorzystując wskaźniki klasyfikacji. Wygenerowaliśmy szczegółowe raporty, aby ułatwić kompleksową analizę porównawczą tych modeli. Warto zauważyć, że XGBoost wykazał najwyższą precyzję na poziomie 97,4%. Ponadto poszliśmy o krok dalej, opracowując model Gated Recurrent Unit (GRU) w celu połączenia przewidywań z alternatywnych modeli. Oceniliśmy jego zdolność do przewidywania nasilenia dolegliwości. Aby określić ilościowo poziomy dokładności modeli w klasyfikacji chorób, obliczyliśmy wartości procentowe nasilenia. Ponadto stworzyliśmy krzywą charakterystyki operacyjnej odbiornika (ROC) dla modelu GRU, upraszczając ocenę jego zdolności do rozróżniania różnych poziomów nasilenia. To kompleksowe podejście przyczynia się do dokładniejszego i bardziej szczegółowego zrozumienia oceny ciężkości choroby Parkinsona

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

Diagnosis of Parkinson’s Disease using Principal Component Analysis and Boosting Committee Machines

Parkinson’s disease (PD) has become one of the most common degenerative disorders of the central nervous system. In this study, our main goal was to discriminate between healthy people and people with Parkinson’s disease. In order to achieve this we used artificial neural networks, and dataset taken from University of California, Irvine machine learning database, having 48 normal and 147 PD cases. We examine the performance of neural network systems with back propagation together with a majority voting scheme. In order to train examples we used boosting by filtering technique with seven committee machines, and principal component analysis is used for data reduction. The experimental results have demonstrated that the combination of these proposed methods has obtained very good results with correct positive value of 92% on the classification of PD.

Neuropsychiatric and cognitive symptoms in Parkinson’s disease: the contribution to subtype classification, to differential diagnosis, their clinical and instrumental correlations

Il piano di ricerca è volto ad approfondire il contributo dei sintomi neuropsichiatrici e cognitivi nelle diverse fasi della Malattia di Parkinson (MP). In particolare, l’argomento di studio è focalizzato sull’analisi dei sintomi cognitivi e neuropsichiatrici nella MP, affrontando queste tematiche anche mediante l’utilizzo di tecniche di neuroimaging, in pazienti drug-naïve, in fase precoce di malattia ed in fase avanzata. Nei pazienti drug-naïve, la ricerca è stata finalizzata alla caratterizzazione dei sintomi neuropsichiatrici e cognitivi nei sottotipi motori (i.e., tremorigeni vs acinetico-rigidi) e rispetto alla lateralità di esordio degli stessi (i.e., lateralità destra vs lateralità sinistra). Nei pazienti in fase precoce di malattia, è stato indagato il contributo dei sintomi neuropsichiatrici e cognitivi nella diagnosi differenziale tra MP e Paralisi Sopranucleare Progressiva (PSP) in pazienti valutati entro i 24 mesi dall’esordio motorio, finestra temporale in cui spesso si assiste ad un overlapping dei sintomi motori. Nei pazienti in fase avanzata di malattia, la ricerca è stata finalizzata alla caratterizzazione, mediante i sintomi neuropsichiatrici e cognitivi, del Gioco D’Azzardo Patologico (gambling) rispetto agli altri tipi di Disturbi del controllo degli Impulsi (ICDs). Ancora nell’ambito dell’ICDs, è stato sviluppato uno studio di neuroimaging, volto ad identificare i correlati morfostrutturali (spessori corticali e volumi dei nuclei sottocorticali) di tali disturbi. Infine, si sono identificati i sintomi neuropsichiatrici e cognitivi che possono impedire l’esecuzione di un esame di Risonanza Magnetica (RM), al fine, in ambito clinico, di preparare adeguatamente all’esame i pazienti più a rischio di mancato svolgimento e con l’intento di indagare, in ambito di ricerca, la reale rappresentatività campionaria dei pazienti inseriti in studi di RM

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

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

Efficacy of Rhythmic Auditory Stimulation on Ataxia and Functional Dependence Post-Cerebellar Stroke

Ataxia, from Greek meaning, “lack of order,” is described as irregular movement and discoordination of body, gait, eyes, and speech. Ataxia is associated with cerebellar damage due to stroke and other cerebellar pathologies. Ataxia frequently results in functional impairment. Standard physical and occupational therapies in stroke rehabilitation facilitate motor recovery, especially within 90 days. However, many patients experience movement derangements beyond this time frame. Rhythmic auditory stimulation has been shown to be an effective intervention in chronic motor deficits like those observed after cerebellar stroke. Efficacy among patients with chronic stroke-induced ataxia is unexplored. This randomized control trial seeks to determine the benefit of rhythmic auditory stimulation over standard of care for rehabilitation of cerebellar stroke-induced ataxia. Patient progress will be assessed using validated disability and ataxia scales. It is projected that rhythmic auditory stimulation will improve ataxia and independence among patients with chronic disability post-cerebellar stroke, versus standard rehabilitation

Physical Therapy Intervention in a Patient with West Nile Meningitis: A Case Approach

Background and Purpose. West Nile Virus (WNV) is a neurotropic virus capable of causing damage of varying severity. WNV is commonly transmitted to humans from mosquitoes and is most prevalent in the months of August and September due to the method of transmission. The WNV can produce mild systemic symptoms classified as West Nile Fever (WNF). However, it can progress and infiltrate the nervous system, at which point the virus is categorized as neuroinvasive. One classification is West Nile Meningitis (WNM) that involves infection and inflammation of the meninges or coverings surrounding the brain and spinal cord. There is currently a limited amount of research related to the presentation and treatment of West Nile Meningitis. The purpose of this case study is to show the potential benefits of physical therapy intervention in order to increase the rate of functional recovery in patients with WNM. Case Description. This case study describes the outpatient physical therapy interventions for an adult female recovering from West Nile Meningitis. The patient presented with poor dynamic balance, decreased coordination, decreased endurance, movement patterns similar to parkinsonism, rigid gait pattern, inability to dual-task, impulsivity and flat affect. Intervention. The patient completed five weeks of outpatient physical therapy. Activities addressed her movement impairments and deconditioning. Exercises were related to strength, coordination, and functional balance with cognitive task integration. Outcomes. The patient returned to work and independent living eight weeks after initial transmission. The patient showed clinically significant improvement on her MiniBESTest scores. She achieved near-baseline functional recovery in significantly less time than previous studies reported. Discussion. Physical therapy intervention and an exercise program to address deconditioning and movement disorders in patients with West Nile Meningitis may significantly increase the rate of functional recovery. Although the primary pathology revolves around the central nervous system, it is important to remember the physical impairments associated with this condition. Physical therapy should be considered an integral part of treatment for cases of West Nile Meningitis