Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis

TL;DR

This study evaluates classical and deep neuroimaging biomarkers for early Alzheimer's detection, demonstrating that combining multiple traditional biomarkers with age improves diagnostic accuracy more than deep learning features alone.

Contribution

It provides a comprehensive comparison of classical and deep learning biomarkers, highlighting the continued relevance of traditional imaging features in early AD diagnosis.

Findings

Radiomics and texture features achieved high AUCs for AD and MCI detection.

Combining multiple biomarkers with age enhances detection performance.

Deep learning features were less effective than traditional biomarkers.

Abstract

Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensitivity and specificity. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers, including radiomics, hippocampal texture descriptors, cortical thickness measurements, and deep learning features. We analyze structural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis and machine learning techniques. Our results show that combining multiple biomarkers significantly improves detection accuracy. Radiomics and texture features emerged as the most effective predictors for early AD, achieving AUCs of 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning features proved to be less effective than traditional approaches, incorporating age with other biomarkers notably enhanced MCI detection performance. Additionally, our findings emphasize the continued importance of classical imaging biomarkers in the face of modern deep-learning approaches, providing a robust framework for early AD diagnosis.