Is the role of artificial intelligence effective in Parkinson's and Alzheimer's diseases?
Is the role of artificial intelligence effective in Parkinson's and Alzheimer's diseases?
Ahmad Nejati Shahidain,1,*Azam Hesami,2
1. Department of Biomedical engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran 2. Lab Solutions Company, located at Science and Technology Park, Shahid Beheshti University
Introduction: Artificial intelligence offers various benefits in the early and accurate diagnosis of neurodegenerative disorders through its ability to analyze data, identify patterns, make predictions, and provide recommendations. This study emphasizes the potential of machine learning and AI in improving the assessment and treatment strategies for these diseases. By utilizing AI, machine learning, signal processing, and computer-aided diagnostic technologies, healthcare professionals can make better clinical decisions. Specifically focusing on Alzheimer's disease and Parkinson's disease, this research aims to explore how AI and machine learning techniques can enhance early detection.
Methods: Deep learning, a notable soft computing approach within machine learning, employs layered algorithmic frameworks known as neural networks. Currently, substantial research efforts are underway to tailor these neural networks for the specific purposes of diagnosing and treating such disorders. These advanced technologies encompass data pre-processing, data collection, machine learning classifiers, and feature extraction techniques. Therefore, there is a critical need for the early detection and intervention of these diseases, which can lead to a moderate enhancement in the quality of life for patients. Significant progress has been achieved in the methods of acquiring neuroimaging data, particularly through diffusion Magnetic Resonance Imaging (MRI) and the analysis of electroencephalogram (EEG) data. To tackle these issues, a variety of machine learning techniques and algorithms are employed, including reinforcement learning, semi-supervised learning, unsupervised learning, supervised learning, deep learning, decision trees, BF trees, bagging, random forest trees, RBF networks, and evolutionary learning.
Results: AD:AI provides tools for analyzing vast and complicated data sets, hence boosting understanding in Alzheimer's disease research. Image and language processing, genetics, and drug development all rely on deep learning. They are multilayer structures in which inputs are routed through balanced sums and nonlinear functions to produce multi-level feature arrangements. Deep learning makes use of vast data to improve efficiency. ADNI datasets enable cooperation, validation of techniques for diagnosis, and discovery of viable therapeutics. Drug exploration, brain imaging, biomarkers, conversion prediction, and disease progression modelling are the key uses of AI in AD research. Big data, neuroimaging, genomics, and fluid biomarkers are all used in these applications. AI and ML approaches can be used to categories AD patients and forecast their future state using MRI scans. The present research used a variety of ML methods, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and AdaBoost. In the early identification and categorization of Alzheimer's disease, the Random Forest classification method has shown substantial performance and outcomes.
PD: Diagnosing Parkinson's disease (PD) solely through clinical methods presents significant challenges due to the disease's clinical variability, the absence of objective biomarkers, and the overlap of symptoms with other medical conditions. Many of these challenges can be addressed by utilizing advanced techniques such as various artificial intelligence (AI) and machine learning (ML) models in the diagnostic process for PD. A notable advancement brought about by AI and ML is the ability to diagnose Parkinson's disease through the analysis of peripheral biopsy samples. Historically, diagnosis relied primarily on clinical symptoms and post-mortem examinations; however, AI now facilitates a non-invasive and cost-effective method by evaluating peripheral biopsy samples, including skin biopsies, colon biopsies, and submandibular gland fluid or blood analyses, which yield highly accurate results with impressive sensitivity levels. Additionally, another AI and ML strategy for diagnosing PD involves the application of AI-driven image analysis to detect PD-related biomarkers. AI algorithms are capable of recognizing specific patterns and markers by analyzing neuroimaging data, thereby enhancing the diagnostic accuracy for Parkinson's disease.
Conclusion: Unlike humans, machine learning algorithms possess the capability to recognize patterns and derive new insights from extensive multidimensional datasets. Nevertheless, the application of machine learning in aiding therapeutic development, prognosis, and diagnosis remains in its early stages. Utilizing medical histories, molecular profiles, imaging data, and the discovery of more specific diagnostic biomarkers, machine learning technology has the potential to facilitate more accurate and timely diagnoses of neurodegenerative diseases in the future. Furthermore, by enhancing patient classification and identifying precise biomarkers for treatment response, machine learning could potentially reduce the time and costs involved in clinical trials while increasing the chances of successful outcomes.