AI Involved in Neuroimaging of Alzheimer’s Disease

Abstract

Artificial intelligence (AI) is transforming the workflow of neuroimaging for the diagnosis of Alzheimer’s disease (AD), especially with the advancement in PET and MRI analysis.    Alzheimer's disease is a progressive neurodegenerative disorder. It is the most prevalent form of dementia, characterized by brain changes related to memory that impede the patient's cognitive functions. From a clinical perspective, early detection of this condition is vital to enhance the likelihood of treating patients who are at risk of further cognitive decline. In a group of studies five topic areas were covered: Multimodal data fusion, image segmentation and preprocessing of brain diagnostic classification, prognosis and disease staging and emerging innovations. Various deep learning models including convolutional neural networks (CNN), Recurrent neural network (RNN) and Generative adversarial networks (GAN) are used in the detection of Alzheimer’s disease from the ADNI PET/MRI dataset. By efficiently processing structural and functional imaging modalities, these models are able to identify pertinent characteristics and patterns linked to Alzheimer’s pathology. When compared to single modality methods, integration to multiple imaging modalities has demonstrated improve in diagnostic accuracy. The pace of growth and development in the area of deep learning for medical analysis is quickly accelerating, paralleling the rising incidence of neurodegenerative disorders. Deep learning models have the potentials to differentiate between Alzheimer’s normal cognitive function and mild cognitive impairment (MCI) with a highest diagnostic accuracy of 96%, but improving models and techniques are essential to overcome obstacles and improve the diagnostic accuracy.