基于深度学习的磁共振成像在阿尔茨海默病诊断中的应用

Application of Imaging-Based Deep Learning in the Diagnosis of Alzheimer's Disease

阿尔兹海默症(AD)作为一种神经退行性疾病,早期阶段往往无明显症状,而当临床症状显现时,病情多已发展至中度或重度,导致患者完全依赖照护者,为护理工作带来极大挑战。因此,AD的早期临床诊断和分期诊断对于患者治疗至关重要。尽管当前已有磁共振成像(MRI)、正电子发射计算机断层显像(PET)等多种影像学技术应用于AD的诊断,但单一影像模态的诊断能力仍有局限。深度学习(DL),作为人工智能的一个重要分支,具备在没有人为干预的情况下,通过神经网络直接从图像中学习和提取特征的能力。近年来,学者们提出了结合MRI、PET等医学影像技术的DL算法,以预测AD的疾病进程。本文首先介绍了深度学习算法的基本概念及其类型,随后详细总结了DL算法与MRI、PET相结合在AD早期诊断与临床分期中展现出巨大潜力,不仅提高了诊断效率,还提升了诊断准确率。最后,本文还预测了未来DL在AD诊断中的发展趋势,并对该领域未来的研究重点进行了概述。

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.