A Local Internet of Medical Things System for Medical Image Analysis in Alzheimer’s Disease Monitoring

Abstract

The integration of the internet of medical things and artificial intelligence technologies is creating a new paradigm in diagnostic medicine. Taking into account growing demands for data security, processing speed, and regulatory restrictions, this article examines the architecture of a local (edge-based) internet of medical things system for analyzing multimodal medical images, such as magnetic resonance imaging and positron emission tomography scans of patients with Alzheimer’s disease. Unlike cloud-based solutions, the proposed system processes data directly at the medical facility, minimizing delays, reducing the risks associated with the transfer of confidential data, and providing complete control over the information. The system utilizes modern convolutional neural networks for automatic segmentation, classification, and multimodal analysis, demonstrating a 15–30 % increase in diagnostic accuracy for neurodegenerative diseases in research settings. The structure and details of the system are developed, and its key components are described. Neural network training and Alzheimer’s disease recognition using these networks are discussed. The advantages of the local approach and the prospects for implementing the internet of medical things system are highlighted.