Utilizing Artificial Intelligence for Early Diagnosis of Neurodegenerative Diseases: A Machine Learning Approach

Abstract

Neurodegenerative diseases, including Alzheimer disease, Parkinson disease and Huntington disease, are challenging the healthcare sector because of their complexities and progressive nature. Diagnosis at an early stage is important to provide an early intervention, yet in the modern approaches to diagnosis, sensitivity and specificity is a primary issue. Artificial intelligence (AI) and machine learning (ML) have seen their recent breakthroughs in recognition and diagnosis of these diseases at an early stage. AI is able to scrutinize the large volumes of medical data to recognize minor patterns and highlight biomarkers that could be overlooked by the other conventional forms of diagnosis. This paper outlines how AI and machine learning can be used to perform an early diagnosis of neurodegenerative diseases, both imaging-based and biomarker-based. It examines different AI algorithms: CNNs (convolutional neural networks), SVM (support vector machine), deep learning models to which medical imaging data (MRI, PET scans) and biomarkers (genomic, proteomic data) are subjected. Also, the paper explains how AI has limitations and pitfalls in neurodegenerative disease diagnosis in terms of data heterogeneity, interpretation, and the requirement of big, high-quality data. The opportunity of AI to transform early diagnosis and make individual approaches to treatment possible is outlined, and how the future of the AI research on neurodegenerative diseases can evolve.