College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
Eur J Radiol. 2023 Aug;165:110934. doi: 10.1016/j.ejrad.2023.110934. Epub 2023 Jun 20.
Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.
阿尔茨海默病是一种主要的神经退行性疾病，主要影响老年人和准老年人。这种进行性神经障碍的特征是一系列症状，包括记忆丧失、认知能力下降以及各种生理和心理障碍，严重影响患者及其护理人员的生活质量。磁共振成像（MRI）技术的最新进展推动了人工智能增强型阿尔茨海默病诊断的研究，为早期检测和及时干预带来了乐观前景。这一进展为开发擅长分析复杂脑成像数据的复杂算法和模型铺平了道路，从而提高了诊断的准确性和效率。这一进步使人们对人工智能驱动的诊断在改变阿尔茨海默病管理方面的变革潜力充满乐观，有望促进更有效的治疗策略和改善患者预后。本综述的目的是全面概述应用于脑 MRI 图像的深度学习方法在分类各种阶段的阿尔茨海默病方面的最新进展，特别强调早期诊断。此外，本综述强调了当前研究的局限性，讨论了这一动态领域的潜在挑战和未来研究方向。
