Using Artificial Intelligence, the Early Detection of Neurological Disorders through Brain Imaging and Neural Data

Abstract

Neurological disorders include Alzheimer\'s disease, Parkinson\'s disease, multiple sclerosis, and epilepsy, which significantly affect millions of people worldwide. Early diagnosis and intervention can drastically improve treatment outcomes, but current diagnostic methods often lack sensitivity and specificity in identifying these conditions in their early stages. Recent advances in artificial intelligence (AI) offer significant potential in addressing these challenges, especially when combined with brain imaging and neural focusing on brain imaging modalities such as MRI, CT, and EEG, and neural signals from devices such as EEG caps and implanted electrodes. It discusses the effectiveness, challenges, and future directions of AI in this domain data. This paper discusses the application of AI techniques, specifically ML and DL, in the early detection of neurological disorders,

Introduction

Neurological disorders such as Alzheimer's disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are chronic and progressive conditions that severely impact cognitive, motor, and sensory functions. These diseases present major challenges to global healthcare systems due to their complexity and the need for early detection to improve patient outcomes.

Traditionally, diagnosing neurological disorders has relied on subjective clinical assessments or invasive procedures, which can delay diagnosis and treatment. Artificial intelligence (AI) is now transforming the field by enabling early, accurate, and non-invasive detection using brain imaging data and neural activity patterns. AI techniques like machine learning (ML) and deep learning (DL) can process complex data from imaging modalities such as MRI, PET, and EEG, identifying subtle disease markers that may be overlooked by human clinicians.

AI’s benefits extend beyond diagnosis. It supports personalized treatment plans, improves decision-making, and enhances care delivery—particularly in under-resourced areas. AI facilitates faster diagnoses and reduces dependency on specialist access by processing large data volumes efficiently. Moreover, it allows real-time monitoring of disease progression, which is key to managing chronic neurological conditions.

The paper also highlights the role of biomarkers—measurable indicators of disease—in early detection. Biomarkers are categorized as:

• Structural (e.g., hippocampal atrophy in AD, substantia nigra loss in PD, lesions in MS via MRI),

• Functional (e.g., disrupted brain connectivity seen through fMRI or EEG),

• Molecular (e.g., tau protein for AD, alpha-synuclein for PD).

Despite technological advances, significant challenges remain:

1. Symptom Overlap: AD, PD, and MS share clinical features, making disease-specific biomarker identification difficult.

2. Data Variability: Differences in patient characteristics and imaging techniques complicate analysis and interpretation.

3. Lack of Standardization: Non-uniform diagnostic assays and protocols hinder cross-study comparisons and clinical adoption.

Efforts are underway to address these issues through standardized testing protocols and international harmonization initiatives.

Conclusion

AI is revolutionizing neurological care by enabling early, precise, and efficient diagnosis and facilitating the move towards personalized medicine. However, for AI and biomarker technologies to become mainstream, challenges like standardization and data consistency must be overcome.

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Copyright

Copyright © 2025 Tanush Arun Kumar, Kavish Alapati. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.