磁共振成像驱动的机器学习用于阿尔茨海默病进展分类：系统评价与荟萃分析

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.

作者信息

Battineni Gopi, Chintalapudi Nalini, Amenta Francesco

机构信息

Clinical Research, Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University Camerino, Camerino, Italy.

Centre for Global Health Research, Saveetha University, Saveetha Institute of Medical and Technical Sciences, Chennai, India.

出版信息

JMIR Aging. 2024 Dec 23;7:e59370. doi: 10.2196/59370.

DOI:10.2196/59370

PMID:39714089

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11704653/

Abstract

BACKGROUND

To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively.

METHODS

The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots.

RESULTS

The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001).

CONCLUSIONS

The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research.

摘要

背景

为了诊断阿尔茨海默病（AD），个体根据其认知障碍的严重程度进行分类。目前该疾病尚无特定病因或条件。

目的

本系统评价和荟萃分析的目的是全面评估使用机器学习（ML）方法在不同阶段的AD患病率。

方法

根据PRISMA（系统评价和荟萃分析的首选报告项目）2020指南，论文选择分三个阶段进行：识别、筛选和最终纳入。最终分析纳入了24篇符合标准的论文。用于AD诊断的ML方法的选择严格基于其与研究的相关性。通过森林图展示了2、3、4和6个阶段AD患者的患病率。

结果

6项研究中认知正常（CN）和AD的患病率为49.28%（95%CI 46.12%-52.45%；P = 0.32）。认知障碍三个阶段（CN、轻度认知障碍和AD）的患病率估计为29.75%（95%CI 25.11%-34.84%，P < 0.001）。在5项有14839名参与者的研究中，对四个阶段（非痴呆、中度痴呆、轻度痴呆和AD）的分析发现总体患病率为13.13%（95%CI 3.75%-36.66%；P < 0.001）。此外，4项涉及3819名参与者的研究估计了六个阶段（CN、显著记忆问题、早期轻度认知障碍、轻度认知障碍、晚期轻度认知障碍和AD）的患病率，患病率为23.75%（95%CI 12.22%-41.12%；P < 0.001）。