Deep learning-based hippocampus asymmetry assessment for Alzheimer's disease diagnosis.

Abstract

Background: The symmetry of the brain hippocampus may be disrupted by natural aging and neurodegenerative diseases.

Purpose: Currently, clinical studies on hippocampus asymmetry are limited to subjective visual evaluation and rough volume measurements, lacking quantitative standards.

Methods: This paper proposes a quantitative assessment method of the hippocampus asymmetry based on deep learning, named DeepHAA (Deep Learning-based Hippocampus Asymmetry Assessment). The DeepHAA model extracts feature representations of left and right hippocampus structures in MRI images and achieved feature fusion through a cross-attention mechanism. A quantitative assessment method is proposed based on the distance between the multimodal embedding of the input sample and the reference embedding space.

Results: The experimental dataset of this paper included MRI scans of 199 subjects, including 53 normal cognition (NC), 71 mild cognitive impairment (MCI) and 33 Alzheimer's disease (AD). The experimental results show that DeepHAA model can effectively identify and distinguish the NC, MCI, and AD.

Conclusions: The proposed deep learning method integrates asymmetric information about hippocampus structure into the diagnosis of AD and has potential clinical application value.