深度学习在老年人轻度认知障碍诊断中应用的Scoping综述

doi:10.3969/j.issn.1006-9771.2025.06.008

《中国康复理论与实践》 ›› 2025, Vol. 31 ›› Issue (6): 674-681.doi: 10.3969/j.issn.1006-9771.2025.06.008

深度学习在老年人轻度认知障碍诊断中应用的Scoping综述

吴晓晖¹, 蒋雷², 朱静茹³, 解利辉⁴

1.首都体育学院实验室与国有资产管理处，北京市 100191
2.首都体育学院体育教育训练学院，北京市 100191
3.首都体育学院运动科学与健康学院，北京市 100191
4.首都体育学院体育人工智能研究院，北京市 100191

收稿日期:2025-04-24 修回日期:2025-04-30 出版日期:2025-06-25 发布日期:2025-06-16
作者简介:吴晓晖(1987-)，男，汉族，福建莆田市人，硕士，助理研究员，主要研究方向：体育人工智能。
基金资助:
国家自然科学基金项目(32371132)

Deep learning for diagnosis of mild cognitive impairment in older adults: a scoping review

WU Xiaohui¹, JIANG Lei², ZHU Jingru³, XIE Lihui⁴

1. Laboratory and State Owned Assets Management Division, Capital University of Physical Education and Sports, Beijing 100191, China
2. Institute of Sports Education and Training, Capital University of Physical Education and Sports, Beijing 100191, China
3. School of Kinesiology and Health, Capital University of Physical Education and Sports, Beijing 100191, China
4. Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China

Received:2025-04-24 Revised:2025-04-30 Published:2025-06-25 Online:2025-06-16
Supported by:
National Natural Science Foundation of China(32371132)

摘要/Abstract

摘要：

目的综述深度学习(DL)在老年人轻度认知障碍(MCI)诊断中的应用与效果。

方法在PubMed、Web of Science、中国知网和万方数据库中，检索建库至2024年12月关于DL在老年人MCI应用方面的文献，并进行Scoping综述。文献筛选流程遵循Scoping综述报告规范清单，并采用循证卫生保健中心开发的横断面研究质量评价工具进行质量评估。

结果共纳入11篇文献，来自意大利、美国、韩国、中国、印度和瑞士，涉及11 829例老年参与者，发表时间集中于2014年至2024年。该领域近10年的快速发展趋势，与DL技术的发展时间一致。纳入文献质量评分均为6~7分。研究类型均为横断面研究，学科交叉特征显著，主要来源于临床医学、生物学、神经影像学等领域。文献数据主要基于阿尔茨海默病神经影像计划数据库，并整合其他数据资源。数据类型方面，除脑影像数据外，还包括1项基于文本数据的研究。使用模型方面，其中5篇研究主要基于卷积神经网络，其余均采用不同的DL模型框架。任务类型包含二分类和三分类。预测结果方面，通过对脑影像等多模态数据构建的DL模型，可构建出高精度的MCI分类预测模型，模型效果均良好，准确率均为70%以上，AUC值均超过0.7。其中部分模型的诊断准确率超过90%，预测准确率最高的模型为使用Biceph-net轻量级框架，准确率接近100%。而基于Transformer的文本分析模型AUC值为0.846，为非影像数据的诊断提供新思路。

结论 DL不仅可为老年人MCI准确识别提供有力支撑，还为临床医生提供了辅助预测工具，有助于延缓病情进展，改善患者预后。

关键词: 老年人, 轻度认知障碍, 深度学习, Scoping综述

Abstract:

Objective To systematically review the application and effectiveness of deep learning (DL) in diagnosis of mild cognitive impairment (MCI) among older adults.

Methods PubMed, Web of Science, CNKI and Wanfang databases were searched for literatures related to the application of DL in MCI among older adults, from database inception to December, 2024. A scoping review was conducted. The literature screening process followed the Scoping Review Report Specification list, and the quality assessment was conducted using the cross-sectional study quality evaluation tool developed by the Evidence-based Health Care Center.

Results A total of eleven papers were included, from Italy, USA, South Korea, China, India and Switzerland, involving 11 829 elderly participants, publicated mainly between 2014 and 2024, reflecting the rapid development trend of the field in the last decade, which was in line with the timing of the development of DL technology. The quality scores of the included literatures were all six to seven. The types of studies were all cross-sectional studies with significant cross-disciplinary characteristics, mainly originating from the fields of clinical medicine, biology and neuroimaging. The literature data were mainly based on the Alzheimer's disease Neuroimaging Program database and integrated other data resources. In terms of data type, in addition to brain imaging data, one study based on text data was also included. In terms of models used, five of the studies were mainly based on convolutional neural networks, and the rest used different DL modeling frameworks. The task types contained binary and triple classification. In terms of prediction results, the DL models constructed on multimodal data, such as brain images, could be used to construct high-precision prediction models for MCI classification, and the models were all good, with accuracy more than 70% and AUC values more than 0.7. The diagnostic accuracy of some of the models was more than 90%, and the model with the highest prediction accuracy was the one that used the Biceph-Net lightweight framework, with accuracy close to 100%, and the text analysis model based on Transformer made the AUC value of 0.846, which provided new ideas for the diagnosis of non-imaging data.

Conclusion DL can not only provide strong support for the accurate identification of MCI in the elderly, but also provide auxiliary prediction tools for clinicians, which can help delay the progression of the disease and improve the prognosis of patients.

Key words: older adults, mild cognitive impairment, deep learning, scoping review

中图分类号:

R749.1

吴晓晖, 蒋雷, 朱静茹, 解利辉. 深度学习在老年人轻度认知障碍诊断中应用的Scoping综述[J]. 《中国康复理论与实践》, 2025, 31(6): 674-681.

WU Xiaohui, JIANG Lei, ZHU Jingru, XIE Lihui. Deep learning for diagnosis of mild cognitive impairment in older adults: a scoping review[J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(6): 674-681.

图/表 3

图1

表1

表2

纳入文献基本特征"

纳入文献	国家	年龄/岁	被试数量	研究方法	数据来源与类型	预测指标	评价指标	深度学习模型
Ciarmiello等^[20](2023)	意大利	72±7.4 MCI: 73.6±8.0 HC:70.8±6.2	n = 328 (MCI = 179；HC = 149) 测试集：15% 训练集：70% 验证集：15%	横断面研究	ADNI数据库；¹⁸F florbetaben PET成像数据	MCI与HC诊断	准确率80% AUC：测试集：0.9 整体样本：0.94	模型：FNN
Pourramezan Fard等^[21] (2024)	美国	75~92	n = 68 (MCI = 34；NC = 34) 5折交叉验证测试集 = 13~14 训练集 = 55~56	横断面研究	I-CONECT项目，对68例75岁及以上老人(分为MCI组和NC组)进行每次30 min、每周4次、共6个月的半结构化视频访谈，采集访谈音频并转录为文本，构建研究数据集	MCI与NC诊断	准确率85.16% AUC：0.85	模型：NLP+MLP 优化器：Adam
Khatri等^[22] (2024)	韩国	56~91	n = 1075 (AD = 390；MCI = 370；HC=315) 测试集 = 107 训练集 = 968	横断面研究	ADNI数据库；sMRI数据	AD、MCI与HC诊断	准确率：多分类：94.31% MCI/HC：92.15% AUC = 0.96	模型：CNN+自注意力机制优化器：AdamW+AdamWGC
Suk等^[23] (2014)	美国	55~89	n = 398 (AD = 93；MCI = 204；NC = 101) 10折交叉验证训练集 = 约358 测试集 = 约40	横断面研究	ADNI数据库；基线的T₁加权sMRI数据、¹⁸F-FDG-PET数据	AD、MCI与NC诊断 MCI-C与MCI-NC区分	MCI/NC：准确率85.67% AUC = 0.8808 MCI-C/MCI-NC：准确率75.92% AUC = 0.7466	模型：DBM
Yoon等^[24] (2024)	韩国	ADNI1(超分辨率) AD：57~91 MCI：60~87 NC：56~88 ADNI1(分类) AD：55~88 MCI：55~89 NC：62~86 ADNI3(分类) AD：56~90 MCI：55~91 NC：52~91	ADNI1(超分辨率) n₁ = 170 (AD = 38；MCI = 54；NC = 78) ADNI1(分类) n₂ = 403 (AD = 95；MCI = 206；NC = 102) ADNI3(分类) n₃ = 1014(AD = 122；MCI = 387；NC = 605) AIBL(外部验证)：n = 116	横断面研究	ADNI数据库和AIBL数据库；1.5 T和3 T的MRI数据	AD与MCI诊断	MCI/NC：准确率85.2% AUC = 0.847 AD/MCI：准确率81.7% AUC=0.813	模型：CNN、diffusion model-based generative AI
Kim等^[25] (2018)	韩国	55~90	n = 202 (HC = 52；MCI = 99；AD = 51) 采用10折交叉验证训练集 = 181 测试集 = 21	横断面研究	ADNI数据库；基线T₁加权sMRI数据、¹⁸F-FDG-PET数据、CSF数据	AD、MCI与HC诊断	MCI/HC：准确率87.09%	模型：MSH-ELM
Suk等^[26] (2016)	韩国、新加坡、美国	ADNI2 MCI：73.9 NC：73.8 In-house MCI：75.0 NC：72.9	ADNI2 n₁ = 62(MCI = 31；NC = 31) In-house n₂ = 37(MCI = 12；NC = 25) 留一法交叉验证	横断面研究	ADNI2数据集和In-house数据集；rs-fMRI数据	MCI与NC诊断	ADNI2：准确率72.58% In-house：准确率81.08%	模型：DAE+HMM
Liu等^[27] (2020)	中国	AD：75.9±6.8 MCI：75.2±7.3 NC：75.9±5.0	n = 449 (AD = 97；MCI = 233；NC = 119) 5折交叉验证训练集 = 89 测试集 = 360	横断面研究	ADNI数据库；基线的T₁加权sMRI扫描数据	AD、MCI与NC诊断	MCI/NC：准确率76.2%，AUC = 0.775	模型：CNN 优化器：Adam
Rashid等^[28] (2023)	印度	未说明	n = 6765 (AD = 1365；MCI = 3100；CN = 2500) 训练集：80% 测试集：20%	横断面研究	ADNI、AIBL、OASIS数据库；2D MRI数据	AD、MCI与NC诊断	MCI/AD：准确率98.16% CN/MCI/AD：准确率97.80%	模型：Biceph-net轻量级框架优化器：Adam
Lee等^[29](2021)	韩国	CN：62~89 平均71.1 eMCI：56~92 平均75.2	n = 101 (eMCI = 53；NC = 48) 10折交叉验证	横断面研究	ADNI数据库；rs-fMRI数据	eMCI与NC诊断	准确率74.42% AUC = 0.7438	模型：CNN+强化学习+GCN 优化器：Adam
Etminani等^[30](2022)	多国合作	ADNI 男性56~92，平均76.7；女性56~96，平均76.2 EDLB 男性48~91，平均72.7；女性50~86，平均72.9	n = 757 (AD = 200；MCI-AD = 200；DLB = 157；NC = 200) 训练集 = 684 测试集 = 73	横断面研究	ADNI数据库和EDLB数据库；¹⁸F-FDG PET扫描数据	AD、DLB、MCI-AD与NC诊断	DLB：AUC = 0.962 MCI-AD：AUC = 0.714 NC：AUC = 0.947	模型：3D CNN 优化器：Adadelta

表2

参考文献 39

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纳入文献	1	2	3	4	5	6	7	8	总分
Ciarmiello等^[20] (2023)	√	√	√	√		√	√	√	7
Pourramezan Fard等^[21](2024)	√	√	√	√			√	√	6
Khatri等^[22](2024)	√	√	√	√			√	√	6
Suk等^[23](2016)	√	√	√	√			√	√	6
Yoon等^[24](2024)	√	√	√	√		√	√	√	7
Kim等^[25](2018)	√	√	√	√		√	√	√	7
Suk等^[26](2014)	√	√	√	√			√	√	6
Liu等^[27](2020)	√	√	√	√	√		√	√	7
Rashid等^[28](2023)	√	√	√	√			√	√	6
Lee等^[29](2021)	√	√	√	√			√	√	6
Etminani等^[30](2022)	√	√	√	√			√	√	6

深度学习在老年人轻度认知障碍诊断中应用的Scoping综述

Deep learning for diagnosis of mild cognitive impairment in older adults: a scoping review

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