Alzheimer's disease(AD),as a neurodegenerative disease,often has no obvious symptoms in its early stage.However,when clinical symptoms appear,the disease has often progressed to moderate or severe stages,leading to complete dependence on caregivers and posing great challenges to nursing work.Therefore,early clinical diagnosis and staging diagnosis of AD are crucial for patient treatment.Although various imaging techniques such as magnetic resonance imaging(MRI)and positron emission tomography(PET)have been applied to the diagnosis of AD,the diagnostic ability of a single imaging modality still has limitations.Deep learning(DL),as an important branch of artificial intelligence,has the ability to learn and extract features directly from images through neural networks without human intervention.In recent years,scholars have proposed DL algorithms combined with medical imaging technologies such as MRI and PET to predict the disease progression of AD.This article first introduces the basic concepts and types of deep learning algorithms,and then summarizes in detail the great potential of DL algorithms combined with MRI and PET in the early diagnosis and clinical staging of AD,which not only improves diagnostic efficiency but also enhances diagnostic accuracy.Finally,this article predicts the future development trend of DL in AD diagnosis and outlines the research priorities in this field in the future.
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