Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis
This addresses early Alzheimer's detection for patients and clinicians, but is incremental as it combines existing biomarkers rather than introducing fundamentally new methods.
This study tackled early Alzheimer's disease diagnosis by extracting and integrating multiple imaging biomarkers from MRI scans, finding that combining biomarkers significantly improved detection accuracy with radiomics achieving an AUC of 0.88 for AD detection and 0.72 for MCI detection.
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.