Research Status of Multimodal Medical Image Fusion In AI-Assisted Diagnosis of Alzheimer's Disease

Zhixing Ni

doi:10.54097/eycysp45

Authors

Zhixing Ni

DOI:

https://doi.org/10.54097/eycysp45

Keywords:

Alzheimer's disease; artificial intelligence; multimodal medical imaging; medical image fusion.

Abstract

As the global population ages, the socioeconomic burden caused by Alzheimer's disease (AD) is rising. Existing clinical interventions mostly focus on delaying the course of the disease and lack support for the accuracy and timeliness of early diagnosis, which has become a key bottleneck restricting the prevention and treatment of AD. This study aims to explore the application value of multi-modal medical image fusion technology in the early diagnosis of AD under the guidance of artificial intelligence (AI), and to verify its advantages and feasibility compared with single-modal imaging diagnosis. The research will integrate multi-modal imaging data such as structural magnetic resonance imaging (sMRI), positron emission tomography (PET) and diffusion tensor imaging (DTI), and complete the extraction, matching and fusion of image features through AI models such as convolutional neural networks (CNN) or Transformer to build an early diagnosis model for AD; and use clinically confirmed AD high-risk groups (patients with mild cognitive impairment) and healthy controls as the research subjects If this study proves feasible, it can provide efficient and accurate technical support for early AD screening, thereby providing a new practical path for delaying the course of the disease and reducing social costs.

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