ARE-PaLED: Augmented Reality-Enhanced Patch-Level Explainable Deep Learning System for Alzheimer’s Disease Diagnosis from 3D Brain sMRI

• Published in: Symmetry (Basel) Vol. 17; no. 7; p. 1108
• Main Authors: S, Chitrakala, U, Bharathi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Advertising executives; Algorithms; Alzheimer's disease; Arkansas; Asymmetry; Atrophy; Augmented Reality; Brain; Deep learning; Diagnosis; Diseases; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Symmetry; Arkansas
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym17071108

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human brain exhibits inherent bilateral symmetry, and deviations from this symmetry—such as asymmetric atrophy—are strong indicators of early Alzheimer’s disease (AD). Patch-based methods help capture local brain changes for early AD diagnosis, but they often struggle with fixed-size limitations, potentially missing subtle asymmetries or broader contextual cues. To address these limitations, we propose a novel augmented reality (AR)-enhanced patch-level explainable deep learning (ARE-PaLED) system. It includes an adaptive multi-scale patch extraction network (AMPEN) to adjust patch sizes based on anatomical characteristics and spatial context, as well as an informative patch selection algorithm (IPSA) to identify discriminative patches, including those reflecting asymmetry patterns associated with AD; additionally, an AR module is proposed for future immersive explainability, complementing the patch-level interpretation framework. Evaluated on 1862 subjects from the ADNI and AIBL datasets, the framework achieved an accuracy of 92.5% (AD vs. NC) and 85.9% (AD vs. MCI). The proposed ARE-PaLED demonstrates potential as an interpretable and immersive diagnostic aid for sMRI-based AD diagnosis, supporting the interpretation of model predictions for AD diagnosis.