Research And Application Analysis of Autism Spectrum Prediction Models Based on Alzheimer's Disease Research Methods

Abstract

Alzheimer's disease (AD) poses substantial challenges due to its complexity and the growing prevalence in aging populations. This paper explores advanced deep learning methodologies applied to the study of AD, extending these techniques to the analysis of Autism Spectrum Disorders (ASD). We begin with an introduction that outlines the urgency of researching neurodegenerative and neurodevelopmental conditions. Section 2 delves into the relevant theories surrounding AD, including its neuropathological and genetic underpinnings. In Section 3, we detail the adaptation of deep learning models originally developed for AD to enhance the understanding and diagnostic capabilities for ASD, highlighting the transferability of these methods. Section 4 further examines the applications of these adapted models in ASD research, showcasing their utility in identifying biomarkers and predicting disease progression. The challenges section discusses the limitations of current models, including data heterogeneity and the need for larger, annotated datasets. The paper concludes with a discussion on future directions, emphasizing the potential for these methodologies to revolutionize our approach to complex neurological disorders. Throughout, this analysis not only underscores the potential of deep learning in medical research but also sets a path for its expanded use in broader neurological studies.