Data Analysis for Neurodegenerative Disorders
- 2023
- Book
- Editors
- Deepika Koundal
- Deepak Kumar Jain
- Yanhui Guo
- Amira S. Ashour
- Atef Zaguia
- Book Series
- Cognitive Technologies
- Publisher
- Springer Nature Singapore
About this book
This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders.
This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features:
● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection.
● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders.
● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.
Advertisement
Table of Contents
-
Frontmatter
-
Overview of Neurodegenerative Disorders
-
Frontmatter
-
Overview of Neurodegenerative Disorders
Shanoo Sharma, Tannu Priya, Neelam Goel, Dharambir Kashyap, Vivek Kumar GargThis chapter offers a detailed overview of neurodegenerative disorders, highlighting their impact on global health and the increasing prevalence due to an aging population. It delves into the pathological features of various disorders such as Alzheimer’s disease, characterized by amyloid plaques and neurofibrillary tangles, and Parkinson’s disease, marked by dopamine insufficiency and motor impairments. The text also discusses Huntington’s disease, Lewy body dementia, cerebral aneurysms, epilepsy, spinocerebellar ataxia, and amyotrophic lateral sclerosis, each with unique symptoms and underlying mechanisms. It explores the multifactorial nature of these disorders, including genetic, environmental, and lifestyle factors. Additionally, the chapter reviews current treatment strategies and the promise of emerging therapies such as gene replacement, stem cell treatments, and nanoparticle-based drug delivery systems. The comprehensive analysis of neurodegenerative disorders and their potential treatments makes this chapter a valuable resource for professionals in the field.AI Generated
This summary of the content was generated with the help of AI.
AbstractNeurodegenerative disorders (NDDs) place a significant medical and public health burden on people all over the world. Three important NDDs are Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), spinocerebellar ataxia (SCA), epilepsy, Lewy body disease, Huntington’s Disorder (HD), and cerebral aneurysm. The number of cases is anticipated to keep increasing in the near future as life expectancies in many nations rise, as the prevalence and incidence of many diseases dramatically increase with age. With a few notable exceptions, it is difficult to determine how genetic and environmental factors interact causally. While identifying high-risk genes for familial NDDs, classifying disease prognostic factors, determining common genetic variants that may predict susceptibility to non-familial forms of these diseases, and quantifying environmental exposures have all been accomplished using molecular epidemiology approaches. Brief overviews of the epidemiologic features of PD, AD, ALS, SCA, epilepsy, Lewy body disease, HD, and cerebral aneurysm, are provided in this chapter, can help in diagnosis of underlying disease and their associated risk factors, potentially improving medical care and, in the end, illness prevention.
-
-
AI and Machine Learning Models for Neurodegenerative Disorders
-
Frontmatter
-
Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative Disorders
Kamini, Shalli RaniThe chapter delves into the use of artificial intelligence and machine learning models for diagnosing neurodegenerative disorders, such as Alzheimer’s and Parkinson’s disease. It discusses the challenges and opportunities in this field, including the potential for early diagnosis, precision medicine, biomarker identification, and drug discovery. The text also highlights specific AI and ML models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and graph convolutional networks (GCNs), and their applications in diagnosing these disorders. Additionally, it covers the methodologies and datasets used in developing these models, providing a detailed and engaging overview of the current state and future prospects of AI in neurodegenerative disorder diagnosis.AI Generated
This summary of the content was generated with the help of AI.
AbstractGradual loss of neuron tissues lying within brain causes abnormality in cognitive and motor which are further responsible for developing neurodegenerative disorders. With the increasing prevalence of these disorders, there is a growing need for accurate and reliable diagnosis, as well as effective treatment strategies. Artificial Intelligence and Machine learning demonstrated great improvement in diagnosing such disorders. Keeping such scenario in mind, AI and ML models can be trained to analyze large datasets of medical imaging and clinical data to identify patterns and biomarkers associated with neurodegenerative disorders. These models can also be used to predict disease progression and response to treatment, enabling personalized care for patients. Some of the Artificial Intelligence (AI) and Machine Learning (ML) models that have been developed for neurodegenerative disorders include deep learning algorithms, graphical convolutional networks etc. for analyzing a variety of data, including structural and functional neuroimaging, genomic data, and electronic health records. While these models have shown promise in improving the diagnosis and management of neurodegenerative disorders, there are also challenges that need to be addressed. These include issues related to data quality, model interpretability, and ethical considerations. Overall, AI and ML models have the potential to revolutionize the field of neurodegenerative disorders, providing clinicians with new tools to improve patient outcomes and enhance our understanding of these devastating diseases. -
Neurodegenerative Alzheimer’s Disease Disorders and Deep Learning Approaches
Bhuvanesh Baniya, Shashikant V. Athawale, Mangi Lal Choudhary, Nema RamThe chapter delves into the challenges and advancements in diagnosing Alzheimer’s disease using deep learning techniques. It introduces the use of 3D-CNN for extracting spatial information from MRI and PET scans, and FSBi-LSTM for temporal memory analysis. The proposed method demonstrates superior performance in classifying different stages of Alzheimer’s disease, including mild cognitive impairment. The chapter also discusses the advantages of this approach over traditional methods, such as better feature extraction and reduced data loss. The results show that the FSBi-LSTM method can accurately diagnose Alzheimer’s disease, making it a promising tool for clinical applications. The chapter concludes by highlighting the potential of this approach for future research and its implications for improving the diagnosis and treatment of Alzheimer’s disease.AI Generated
This summary of the content was generated with the help of AI.
AbstractConvolutional neural networks (CNN) can no longer make a significant contribution to Alzheimer’s disease diagnosis because there is insufficient data to work with. We have built a cutting-edge deep learning system and are currently putting it to use to increase the effectiveness of the work we are doing to achieve this goal. To achieve the highest level of performance, we combine the advantages of fully stacked bidirectional long short-term memory (FSBi-LSTM) with those of three-dimensional convolutional neural networks. These two methods of data storage are stacked one on top of the other. Before interpreting the MRI and PET images, it is critical to train a three-dimensional convolutional neural network. This must be completed to proceed to the next stage. The essential qualities of the deep features can be agreed upon. Before any further inquiry into the matter can proceed, this must be done. Here is only one example of how this method may be applied. Even if only one individual is made aware of this, the ramifications might be terrible. Lastly, we compared our findings to those of an Alzheimer’s disease neuroimaging research study to show definitively that our technique is beneficial in Alzheimer’s disease management. According to our observations, our approach surpasses other theoretically comparable algorithms published in academic literature. These algorithms were evaluated based on their ability to tackle the same issue. This is true regardless of whether our technique is technically equivalent to other published methods: cases of pMCI can be distinguished from NC with a success rate of 94.82%; cases of sMCI can be distinguished from NC with an 86.30% success rate; and cases of Alzheimer’s Disease (AD) can be distinguished from NC with an 86.30% success rate. This result was obtained despite the fact that there was inadequate imaging evidence to back it up. -
Yoga Practitioners and Non-yoga Practitioners to Deal Neurodegenerative Disease in Neuro Regions
Amar Shukla, Shamik Tiwari, Vinh Truong HoangThe chapter delves into the neuroprotective effects of yoga, particularly focusing on its impact on the cortex and other brain regions. It explains how yoga can reduce the risk of neurological diseases such as Parkinson’s, Alzheimer’s, and multiple sclerosis by strengthening the cortex, white matter, and grey matter. The text also explores advanced MRI techniques and machine learning algorithms used to measure cortical thickness and predict brain age. Additionally, it highlights recent studies demonstrating the positive effects of yoga on brain function and mental health, making it a valuable resource for both practitioners and researchers in the field.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe ultimate goal of the research study is to determine if regular practice of yoga has any beneficial effects on brain functions and neurodegenerative diseases. To this end, advanced imaging techniques such as the grey matter volume and cortical thickness will be employed, along with machine learning algorithms to evaluate cognitive performance. Furthermore, it will help assess the neuro-regions, white matter, and cortex which affect the brain age and compare it to their actual age. The results of this study can provide a better understanding of how much yoga can benefit day-to-day behavior and life cycles. A further implication of this study is that it could also contribute insights into the role yoga plays in the prevention of neurodegenerative diseases.
-
-
Machine Learning Models for Alzheimer’s Disorders
-
Frontmatter
-
Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease
Sarika Khandelwal, Harsha R. Vyawahare, Seema B. RathodThe chapter delves into the pressing issue of Alzheimer's disease (AD), highlighting the urgent need for early diagnosis to prevent its progression. Traditional diagnostic methods, such as neuropsychological testing and medical imaging, are discussed along with their limitations. The focus shifts to the potential of electroencephalogram (EEG) signal processing, particularly of the temporal lobe, to detect AD at its earliest stages. The chapter explores the complex nature of brain signals and their frequency bands, emphasizing the importance of accurate correlation and deviation analysis. The proposed approach using EEG signals offers a promising, non-invasive, and cost-effective method for early AD diagnosis, with the potential to revolutionize disease management and patient outcomes.AI Generated
This summary of the content was generated with the help of AI.
AbstractNowadays early diagnosis of cognitive disease is a challenge because of lifestyle and stressful life. It is not easy to differentiate stress and Alzheimer condition from mere observation of personality. Detection of Alzheimer at the early stage is crucial to avoid later issues that include memory loss, person getting bedridden etc. The existing methods to detect Alzheimer is mostly expensive and requires laborious analysis which may result in delay for the start of actual medical treatment. Even some of the methods are invasive too. Hence there is demand to automate the detection of Alzheimer at the early stage using some noninvasive methods. In this chapter, the authors have suggested a strategy for the cognitive study of brain signals with Electroencephalogram (EEG) device. Collection of EEG signals are noninvasive and not much expensive. In our study, we have conducted experimentation for the temporal lobe. EEG signals originating from temporal lobes are down-sampled to 5 bands of varying frequency and further used as a dataset for the classification model. Five datasets were created from 5 different bands are used for training and testing purpose in proportion of 70–30 respectively. We have achieved praiseworthy accuracy for theta band for diagnosing of Alzheimer disease at early stage. We have achieved 98% accuracy by using deep learning model with 8 layers and activation function ReLu. -
Machine Learning Models for Alzheimer’s Disease Detection Using OASIS Data
Rajesh Kumar Shrivastava, Simar Preet Singh, Gagandeep KaurThe chapter delves into the application of machine learning models for early detection of Alzheimer’s disease using MRI data from the OASIS dataset. It begins with an introduction to Alzheimer’s disease, its symptoms, and the importance of early detection. The authors then compare different machine learning algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, and Random Forests, evaluating their accuracy and performance metrics such as AUC, precision, and recall. The chapter also includes a detailed analysis of the dataset, pre-processing steps, and the results of the machine learning models. The authors conclude with a discussion on the future directions of this research, highlighting the potential of deep learning approaches with larger datasets. This chapter is a valuable resource for professionals seeking to understand the practical applications of machine learning in healthcare and the specific challenges of detecting Alzheimer’s disease.AI Generated
This summary of the content was generated with the help of AI.
AbstractEarly Prediction of Alzheimer’s disease is a challenging task for researchers to contribute. Dementia is the simplest symptom of Alzheimer’s disease. Nowadays, most researchers apply Artificial Intelligence to discover mental disorders like Alzheimer’s, which mostly affect the old age population worldwide. In Alzheimer's disease, the brain is under neurodegenerative changes. As our population ages, more people will be affected by diseases that impact memory functionalities. These repercussions will profoundly affect the person’s social and financial fronts. It is difficult to predict Alzheimer's disease in its early stages. The Medication given early in Alzheimer's disease is more effective and has fewer minor side effects than treatment given later. To find the optimum parameters for Alzheimer's disease prediction, researchers used a variety of algorithms, including Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, and Voting classifiers. Predictions of Alzheimer's disease are based on data from the Open Access Series of Imaging Studies (OASIS). The performance of machine learning models is tested using measures such as Precision, Recall, Accuracy, and F1-score. Clinicians can use the proposed classification approach to make diagnoses of these disorders. With these ML algorithms, it is extremely beneficial to reduce annual Alzheimer's disease death rates in early diagnosis. On the test data of Alzheimer’s disease, the proposed work demonstrates better results, with the best validation average accuracy of 80%. -
Electroencephalogram Analysis Using Convolutional Neural Networks in Order to Diagnose Alzheimer’s Disease
David Benavides López, Angela Díaz-Cadena, Yelena Chávez Cujilán, Miguel Botto-TobarThe chapter delves into the application of Convolutional Neural Networks (CNNs) for analyzing electroencephalogram (EEG) data to diagnose Alzheimer’s disease. It begins by introducing the challenges of dementia and Alzheimer’s disease, highlighting the need for accurate diagnostic tools. The text then explores the advantages of EEG analysis, such as high temporal resolution and non-invasiveness. It discusses various EEG frequency bands and their significance in diagnosing Alzheimer’s. The chapter also covers the use of machine learning algorithms, including CNNs, to classify EEG signals and improve diagnostic accuracy. The proposed methodology involves feature extraction from EEG data and training a CNN model to categorize patients. The results section presents the performance of different classification algorithms, with CNNs showing superior accuracy. The chapter concludes by discussing the implications of these findings for the future of Alzheimer’s diagnosis and the potential of deep learning in neuroscience.AI Generated
This summary of the content was generated with the help of AI.
AbstractRecent technological advances have made it possible to collect biomarkers in the same geographic areas where a disease's earliest symptoms occur. Recent technical advances have enabled the collection of biomarkers in areas where early symptoms co-occur. This goal, which is important for finding Alzheimer's disease and its symptoms quickly and accurately, could be achieved in a way that is helpful. It is critical to attain this goal to have a way to treat Alzheimer’s disease and its symptoms. It is critical to recognize Alzheimer's disease and its symptoms accurately and immediately, while also maintaining a high degree of diagnostic accuracy. This goal's significance cannot be overemphasized. This severe impediment must be overcome to go forward. It will be critical to monitor the postsynaptic potential of hundreds of neurons grouped in the same spatial orientation to progress in this direction. This enables the calculation of the entire amount of time that the electrical activity happened during the measurement. This is since the total length of time may be calculated. Time-dependent power spectrum descriptors were employed in this study to provide a differential diagnosis of electroencephalogram signal function. This chapter will be delivered to you as verification of the accomplishments. You will be given this information in the form of a record after the findings have been tallied. Convolutional neural networks will be the focus of the third phase of the discussion on how to categorize people with Alzheimer’s disease. Following that, we'll conclude our investigation into this topic. These networks have just recently, if at all, been created and placed into service. Analyzing the data indicated that the initiative was a resounding and unequivocal success in every way. The absence of negative outcomes may lead to this conclusion. Look at this fantastic example: When convolutional neural networks are used as the analytical technique, the concept of correctness is accurate to an accuracy precision of 82.3%. This demonstrates that our understanding of the concept is correct. There was a lot of success in terms of obtaining the desired degree of precision. Only 85% of cases with moderate cognitive impairment are fully and totally recognized, compared to 75% of the population that is healthy and 89.1% of cases associated with Alzheimer’s disease. -
Alzheimer’s Disease Diagnosis Assistance Through the Use of Deep Learning and Multimodal Feature Fusion
Angela Díaz-Cadena, Irma Naranjo Peña, Hector Lara Gavilanez, Diana Sanchez Pazmiño, Miguel Botto-TobarThe chapter delves into the neurodegenerative nature of Alzheimer’s disease and the challenges in its diagnosis. It discusses the role of structural MRI and PET imaging in identifying the disease, with a particular focus on grey matter tissue. The authors propose a multimodal approach using deep learning to fuse MRI and PET data, aiming to improve diagnostic accuracy. The methodology involves image registration, segmentation, and the application of convolutional neural networks to analyze the fused data. The chapter also presents results from experiments and comparisons with other diagnostic techniques, demonstrating the potential of the proposed method. The authors conclude by highlighting the advantages of their approach and its potential for clinical application.AI Generated
This summary of the content was generated with the help of AI.
AbstractPatients suffering from Alzheimer's disease (AD) lose their ability to think and frequently forget what they have learned during their life. There are currently no effective therapies available for this illness. The sooner the disease is recognized, the better the therapy alternatives and the greater the possibility of eliminating Alzheimer’s. Computer-assisted diagnosis, or CAD, is a method that integrates neuroimaging with deep learning algorithms trained on multimodal pictures. The CAD system is powered by deep learning algorithms that were trained to function by being exposed to a diverse spectrum of artistic outputs. Each component of the system affects the functioning of these algorithms. In recent years, several multimodal feature learning-based alternative techniques for extracting and integrating latent. We were able to achieve our aim because we devised several novel approaches. Here are some more detailed illustrations of imaging techniques: This diagnostic category includes imaging procedures such as MRI and PET scans. Given the complexities of the procedures utilized, providing a complete assessment of the immeasurable value of the data obtained is difficult. An image-based multimodal fusion approach is proposed as a result, our understanding of the brain’s structure and operation has grown significantly. The technique's primary emphasis is the grey matter of the brain. We were able to provide more accurate diagnoses to individuals suffering from neurological diseases. To accomplish our purpose, we use the registration and mask coding procedures. This had a direct impact on the creation of a well-rounded theory aimed primarily at the automobile sector. In addition, we put our image fusion approach to the test with a 3D basic convolutional neural network for binary classification and a 3D multi-scale CNN for multiple classification tasks. These two networks are linked by the fact that they are 3D convolutional neural networks. In a three-dimensional situation, both functions admirably. Using the ADNI dataset, researchers revealed that their suggested picture fusion algorithm outperformed cutting-edge approaches for detecting Alzheimer's disease. Furthermore, as compared to feature fusion and single-modal approaches, its overall performance is significantly superior. -
Machine Learning Models for Alzheimer’s Disease Detection Using Medical Images
Yusera Farooq Khan, Baijnath Kaushik, Deepika KoundalThis chapter delves into the innovative applications of machine learning and deep learning models for the early detection of Alzheimer’s disease using medical images. It begins with an introduction to the transformative potential of artificial intelligence in healthcare, highlighting the exponential growth in the adoption of machine learning and deep learning algorithms for neuroimaging. The chapter explores the underlying causes and consequences of neurodegeneration, with a particular focus on Alzheimer’s disease. It discusses the various medical imaging techniques, such as MRI, CT, and PET, that are crucial for diagnosing neurodegenerative disorders. The review also covers the use of artificial intelligence in computer-aided diagnosis, emphasizing the superiority of deep learning algorithms in feature extraction and classification. The chapter concludes by underscoring the importance of early detection and diagnosis in managing Alzheimer’s disease and the promising future of AI-driven medical imaging in healthcare.AI Generated
This summary of the content was generated with the help of AI.
AbstractHuman brain is an exclusive, sophisticated, and intricate structure. Neuro-degeneration is the death of neurons which is the ultimate cause of brain atrophy resulting in multiple neurodegenerative diseases. Neuro-imaging is the most critical method for the detection Alzheimer’s and quantification of brain atrophy. Magnetic resonance imaging (MRI), computed tomography (CT), single-photo emission computes tomography (SPECT), and positron emission tomography (PET) are the widely used neuroimaging techniques to image/estimate altered brain tissue and to assess neurodegeneration associated with Alzheimer’s. Traditionally, neuro-radiologists incorporate clinically useful information and medical imaging data from various sources to interrelate the structural changes, reduction in brain volume, or changes in patterns of brain activity. In recent years, machine learning and artificial intelligence-based approaches continue to garner substantial interest in neurobiology domains and have emerged as powerful tools for the efficient prediction of neurological and psychiatric disorder-related outcomes. Traditional machine learning algorithms show limitations in terms of the data size and image feature extractions. To address such concerns, Deep Learning Algorithms relying on Deep Convolution Networks (DCN) and Recurrent Neural-inspired Networks (RNN) have advanced to more powerful paradigms to solve the complexity of multistate brain imaging data and to provide extensive solutions in the better understanding of mechanistic details of the progression of brain atrophy in Alzheimer’s disease. The rationale of this study is to provide an in-sight to role of Machine learning for AD detection using neuroimaging data. -
Machine Learning Models for Diagnosing Alzheimer’s Disorders
Kamini, Shalli RaniThis chapter delves into the critical role of machine learning in diagnosing Alzheimer's disease, a significant public health issue affecting millions worldwide. By analyzing patterns in neuroimaging data and cognitive test results, machine learning models can accurately predict the onset of the disease years before clinical symptoms appear. The chapter reviews various datasets, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging, which have been instrumental in developing and evaluating these models. It also explores different machine learning methodologies, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Deep Belief Networks (DBNs), and their architectures, such as multi-modal deep learning and graph convolutional networks. The chapter discusses the contributions of these models, including improved diagnosis accuracy, early detection, personalized medicine, and reduced costs. It also addresses the challenges and opportunities in this field, such as the need for interpretability and standardization of data. Overall, the chapter underscores the transformative potential of machine learning in enhancing the diagnosis and management of Alzheimer’s disease, paving the way for better patient outcomes and quality of life.AI Generated
This summary of the content was generated with the help of AI.
AbstractAlzheimer’s disease is a neurodegenerative disorder which affects millions of people around the world. Machine learning (ML) models have emerged as a promising tool in early diagnosis and prediction of Alzheimer. One popular approach is to use neuroimaging data to train ML models to classify individuals as either having Alzheimer’s disease or being healthy. These models can also be used to predict the progression of the disease in individuals over time. Another approach is to use ML models to analyze data from cognitive tests to identify patterns that may indicate the onset of Alzheimer. ML models can also be used to develop personalized treatment plans for individuals based on their cognitive profile. While there is still much work to be done in this field, ML models show great potential for improving our understanding and management of Alzheimer’s disease. These models have the potential to provide more accurate and timely diagnoses, identify at-risk individuals earlier, and improve treatment outcomes. -
Alzheimer’s Disease Diagnosis Using MRI Images
Mahmoud Ahmad Al-Khasawneh, Abdulrahman Alzahrani, Alaa AlaroodThe chapter delves into the diagnosis of Alzheimer's disease using MRI images and deep learning techniques. It introduces various feature extraction methods, such as 2-D slices, 3-D global slices, and 2-D regions of interest. The study focuses on a self-operating diagnostic system based on multichannel contrastive learning and 3-D convolutional neural networks (3-D CNN). The research highlights the superiority of multichannel contrastive learning over traditional self-supervised methods, demonstrating improved accuracy in diagnosing Alzheimer's disease and mild cognitive impairment. The chapter also explores data transformation techniques and their impact on network performance, concluding with a comparison of the proposed method with existing approaches.AI Generated
This summary of the content was generated with the help of AI.
AbstractAs one gets older, the likelihood of this happening increases. Minor cognitive impairment may serve as an early warning sign of dementia, according to popular belief. Some of the claims stated here are not supported by sources. Alzheimer’s disease and other kinds of neurodegeneration frequently manifest their first signs as dementia. People with poor intellect may struggle to discern between a sick and a healthy reference group. Our system can efficiently extract as much data from the scan as was feasible by using the entire three-dimensional magnetic resonance imaging images as input. The multichannel meta-discourse learning approach improves the system’s and network’s categorization abilities as well as their ability to make large generalizations. One option for achieving this goal is to broaden the number of available educational routes. The binarization loss and the unchaperoned contrastive loss are combined into a single loss to achieve this. Combining the binarization loss and the unchaperoned contrastive loss results is a realistic method that may help you achieve your aim. We ran many experiments using the acquired data to put our system through its paces and verify its value. These findings show that our method might be used to diagnose Alzheimer's disease, dementia, and mild cognitive impairment (MCI). Individual adaptation to their preferred mode of learning is one method that, if implemented, has the potential to significantly boost the value of educational experiences. By creating a fresh perspective on education, the network may be able to assist us in classifying data in a more accurate and appropriate manner in a wider range of scenarios.
-
-
AI Based Diagnosis of Parkinson’s Disorders
-
Frontmatter
-
The Colossal Impact of Machine Learning Models on Parkinson’s Disorder: A Comparative Analysis
Tapan Kumar, R. L. UjjwalThe chapter delves into the increasing prevalence of Parkinson's disease due to ageing populations and sedentary lifestyles. It compares multiple machine learning models for early disease prediction, using a dataset from the University of California, Irvine. The study includes a detailed bibliometric analysis using the VOSviewer tool and technical reviews of recent research. Key authors and keywords in the field are identified, and the accuracy of various machine learning models is evaluated. The chapter concludes with a discussion on the potential of machine learning models in predicting Parkinson's disease and suggests future directions for improving model accuracy.AI Generated
This summary of the content was generated with the help of AI.
AbstractThere are many developing and developed countries with ageing populations. The median age of the population of Monaco, which is among the highest, is 55 years old. Populations in Japan, Germany, Italy, Hong Kong, and Greece have median ages exceeding 45. Parkinson's disease is one of several illnesses that are increasingly prevalent as people get older. Parkinson's disease is one of the most common neurological conditions, impacting the majority of people worldwide. Early generations lead more active lives because of conventional work-related responsibilities, but as quality of life has improved and technology has been integrated, people's lifestyles have grown more sedentary. In the past 25 years, several diseases have been diagnosed as a result of the sedentary lifestyle's detrimental effects on health. One of the diseases that have grown by more than twice as much is Parkinson’s disease (Ciobanu et al. in Exp Ther Med 22:1–7, 2021 [1]). Motor neurons are a prominent source of concern in Parkinson's disease. Early signs and symptoms are neglected by the patient, who is unable to feel them, and the doctor is also in the dark because the early symptoms are not properly diagnosed by many laboratory tests. In this chapter, various machine learning models are compared and examined. The data is taken from the University of California, Irvine’s data repository for comparative analysis and model evaluation. -
Artificial Intelligence Based Diagnosis of Parkinson’s Disorders
Kamini, Shalli Rani, Ali Kashif BashirThis chapter delves into the innovative application of artificial intelligence (AI) for diagnosing Parkinson’s disease (PD), a neurodegenerative disorder affecting millions worldwide. The introduction outlines the challenges of traditional PD diagnosis, which relies on patient history, physical examination, and imaging studies. AI emerges as a promising solution, using intelligent algorithms to analyze vast datasets of patient information, including medical records, imaging data, and patient histories. The chapter discusses AI-based medical examinations, such as the Parkinson’s KinetiGraph (PKG) system, which monitors motor signs and provides detailed reports for physicians. Additionally, it explores the use of machine learning algorithms to analyze brain imaging data and voice patterns for early PD detection. Various AI models, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) Networks, are compared for their accuracy in diagnosing PD. The chapter also highlights the potential of AI in providing early detection, non-invasive diagnosis, objective measurements, and personalized treatment plans. Challenges and opportunities in AI-based PD diagnosis are discussed, emphasizing the need for standardization, data collection, and ethical considerations. The chapter concludes by highlighting the potential of AI to revolutionize PD diagnosis and management, ultimately improving patient outcomes and quality of life.AI Generated
This summary of the content was generated with the help of AI.
AbstractParkinson is a neurodegenerative disorder which affects a considerable fraction of the global population. Early and accurate diagnosis of Parkinson is essential for proper treatment and disease management. Artificial intelligence (AI) has emerged as a promising tool in the field of medical diagnosis, including PD. AI algorithms can analyze large datasets of patient information, including medical records, imaging data, and patient histories, to identify patterns and predict the likelihood of PD. Machine learning (ML) and deep learning (DL) algorithms have been trained on various data sources to diagnose PD with high accuracy, sensitivity, and specificity. AI-based approaches to PD diagnosis have also led to the development of new tools, including wearable sensors and mobile apps that can monitor patients’ movements and track changes in their condition. While AI-based PD diagnosis is still in its early stages, the potential for this technology to improve patient outcomes is significant. However, it is essential to continue improving AI algorithms and incorporating them into clinical practice to ensure their safety and effectiveness while diagnosing Parkinson and its treatment. -
An Artificial Intelligence Based Effective Diagnosis of Parkinson Disease Using EEG Signal
Mahmoud Ahmad Al-Khasawneh, Abdulrahman Alzahrani, Alaa AlaroodThe chapter delves into the intricate workings of the human brain, emphasizing the significance of the brain's electrical activity in diagnosing neurological disorders. It introduces Parkinson's disease (PD) and its debilitating symptoms, highlighting the loss of dopamine and its impact on motor functions. The use of electroencephalogram (EEG) signals is explored as a non-invasive diagnostic tool, with a focus on the challenges and potential of EEG in clinical applications. The chapter also discusses the role of artificial intelligence (AI) in enhancing EEG analysis, offering a promising avenue for early and accurate PD diagnosis. The research work proposes a mathematical model combining EMG and EEG data, demonstrating high accuracy in distinguishing PD patients from healthy individuals. This model holds potential for monitoring disease progression and treatment effectiveness, making it a valuable tool for clinicians and researchers alike.AI Generated
This summary of the content was generated with the help of AI.
AbstractThis study focuses on the use of human bio-signals for the early diagnosis of PD (Parkinson’s disease). EEG (Electroencephalography) and EMG have been used to examine human brain and muscle signals to learn more about the functional and neurological alterations of Parkinson’s patients. Parkinson disease (PD) is a neurological illness that typically affects people over the age of 50. Dopamine, a neurotransmitter, is depleted in the substantia nigra as a result. As this neurotransmitter is released, the person’s muscles begin to contract. Reduced dopamine production causes a loss of brain and muscle coordination, which manifests as unsteady limb movement in a person with PD. The underlying aetiology of PD can be validated by studying the functional and neural alterations using EEG and correlating the results with EMG. It will explain the origin of the wide range of early-stage motor and non-motor PD symptoms. The EEG and EMG results for detecting early-stage PD were validated using other radiological data, such as a Brain Magnetic Imaging signal. The mathematical model for PD diagnosis was developed utilising an ANN and a graphical user interface. The ANN-designed classifier achieved a near-perfect accuracy rate of 100% while testing its ability to distinguish between an early-stage PD patient and a control subject using a dataset consisting of electroencephalogram and electromyogram readings as input features.
-
-
Conclusions and Future Perspectives for Automated Neurodegenerative Disorders Diagnosis
-
Frontmatter
-
Future Perspectives for Automated Neurodegenerative Disorders Diagnosis: Challenges and Possible Research Directions
Attuluri Vamsi Kumar, Sunil Kumar, Vivek Kumar Garg, Neelam Goel, Vinh Truong Hoang, Dharambir KashyapThe chapter delves into the potential of AI and machine learning for automating the diagnosis of neurodegenerative disorders, such as Alzheimer's and Parkinson's disease. It discusses the challenges faced in this field, including the need for large and diverse datasets and the difficulty of obtaining accurate medical imaging data. Recent developments, such as deep learning, transfer learning, and multimodal medical image fusion techniques, are highlighted as promising avenues for improving diagnostic accuracy. The chapter also explores future research directions, emphasizing the need for more rigorous evaluations of AI models in clinical settings, integration with other diagnostic modalities, and the development of AI-based decision support systems for clinicians. The text concludes by underscoring the transformative potential of AI in enhancing diagnostic accuracy, reducing healthcare burdens, and improving patient outcomes.AI Generated
This summary of the content was generated with the help of AI.
AbstractArtificial intelligence (AI) and machine learning (ML) models have been increasingly used in the diagnosis of neurodegenerative disorders. These models have the potential to improve diagnostic accuracy, reduce the burden on healthcare systems, and improve patient outcomes. However, there are several challenges that need to be addressed for these models to be widely adopted in clinical practice. This article provides a summary of the current state of AI and ML models for neurodegenerative disorder diagnosis, including their strengths and limitations. It also discusses the challenges faced in the field, such as the need for large and diverse datasets, the difficulty of obtaining accurate and reliable medical imaging data, and the need for robust and interpretable models. Furthermore, it gives an overview of the recent developments in the field such as the use of deep learning, transfer learning, and multimodal medical image fusion techniques for the diagnosis of neurodegenerative disorders. The article highlights the need for more research and development in the field, specifically in areas such as the integration of multiple data modalities, the use of explainable AI for clinical decision making, and the development of personalized treatment plans. Finally, it suggests future research directions for the field, such as the need for more rigorous evaluation of AI models in clinical settings, the integration of AI with other diagnostic and therapeutic modalities, and the development of AI-based decision support systems for clinicians.
-
- Title
- Data Analysis for Neurodegenerative Disorders
- Editors
-
Deepika Koundal
Deepak Kumar Jain
Yanhui Guo
Amira S. Ashour
Atef Zaguia
- Copyright Year
- 2023
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-9921-54-6
- Print ISBN
- 978-981-9921-53-9
- DOI
- https://doi.org/10.1007/978-981-99-2154-6
PDF files of this book don't fully comply with PDF/UA standards, but do feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com
