人工智能在阿尔茨海默病全程管理中的应用进展

doi:10.3969/j.issn.1003-9198.2025.08.003

摘要/Abstract

摘要： 本文介绍了人工智能(artificial intelligence,AI)在AD的早期筛查与诊断、AD病人的干预与照护中的应用进展,指出AI可以协助AD的早期诊断与药物全过程的研发、发现AD的潜在风险、生成个性化的治疗方案、更准确地预测疾病结果,最终提高AD病人的生活质量。但是,AI在AD的全程管理中仍然存在伦理考量、数据隐私问题以及如何将AI无缝整合到临床工作流程中的挑战。

关键词: 阿尔茨海默病, 人工智能, 医学影像, 生物标志物, 疾病管理

Abstract: This review introduces the advances in the application of artificial intelligence (AI) in the early screening and diagnosis of Alzheimer’s disease (AD), as well as in the intervention and care for AD patients. It highlights that AI can assist in the early diagnosis of AD and the entire drug development process, identify the potential risks of AD, generate personalized treatment plans for AD patients, and more accurately predict the disease outcomes, ultimately improve the quality of life in AD patients. However, AI in the full-course management of AD still faces challenges, including ethical considerations, data privacy, and the seamless integration of AI into clinical workflows.

Key words: Alzheimer’s disease, artificial intelligence, medical imaging, biomarker, disease management

中图分类号:

R749.16

杨慧娟, 刘顺英, 苏晶, 曾利婷. 人工智能在阿尔茨海默病全程管理中的应用进展[J]. 实用老年医学, 2025, 39(8): 768-772.

YANG Huijuan, LIU Shunying, SU Jing, ZENG Liting. Advances in the application of artificial intelligence in the full-course management of Alzheimer’s disease[J]. Practical Geriatrics, 2025, 39(8): 768-772.

参考文献

[1] 王刚, 齐金蕾, 刘馨雅,等.中国阿尔茨海默病报告2024[J]. 诊断学理论与实践, 2024, 23(3): 219-256.
[2] PASSERI E, ELKHOURY K, MORSINK M, et al. Alzheimer’s disease: treatment strategies and their limitations[J]. Int J Mol Sci, 2022, 23(22):13954.
[3] SØRENSEN L, IGEL C, HANSEN N L, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture[J]. Hum Brain Mapp, 2016, 37(3): 1148-1161.
[4] DE OLIVEIRA M S, BALTHAZAR M F, D’ABREU A, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease[J]. AJNR Am J Neuroradiol, 2011, 32(1): 60-66.
[5] JIN D, ZHOU B, HAN Y, et al. Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease[J]. Adv Sci, 2020, 7(14): 2000675.
[6] FALGÀS N, SÁNCHEZ-VALLE R, BARGALLÓ N, et al. Hippocampal atrophy has limited usefulness as a diagnostic biomarker on the early onset Alzheimer’s disease patients: a comparison between visual and quantitative assessment[J]. Neuroimage Clin, 2019, 23: 101927.
[7] GALTON C J, GOMEZ-ANSON B, ANTOUN N, et al. Temporal lobe rating scale: application to Alzheimer’s disease and frontotemporal dementia[J]. J Neurol Neurosurg Psychiatry, 2001, 70(2): 165-173.
[8] FALGÀS N, BALASA M, BARGALLÓ N, et al. Diagnostic accuracy of MRI visual rating scales in the diagnosis of early onset cognitive impairment[J]. J Alzheimers Dis, 2020, 73(4): 1575-1583.
[9] KOIKKALAINEN J R, RHODIUS-MEESTER H F M, FREDERIKSEN K S, et al. Automatically computed rating scales from MRI for patients with cognitive disorders[J]. Eur Radiol, 2019, 29(9):4937-4947.
[10] MÖLLER C, PIJNENBURG Y A L, VAN DER FLIER W M, et al. Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis[J]. Radiology, 2016, 279(3): 838-848.
[11] HOSSEINI-ASL E, GHAZAL M, MAHMOUD A, er al. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network[J]. Front Biosci: Landmark Ed, 2018, 23(3):584-596.
[12] LIU S, LIU S, CAI W, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease[J]. IEEE Trans Biomed Eng, 2015, 62(4): 1132-1140.
[13] GRUESO S,VIEJO-SOBERA R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review[J]. Alzheimers Res Ther, 2021, 13(1): 162.
[14] DREW P J. Vascular and neural basis of the BOLD signal[J]. Curr Opin Neurobiol, 2019, 58: 61-69.
[15] JU R, HU C, ZHOU P, et al. Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning[J]. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(1): 244-257.
[16] MEIER T B, DESPHANDE A S, VERGUN S, et al. Support vector machine classification and characterization of age-related reorganization of functional brain networks[J]. Neuroimage, 2012, 60(1): 601-613.
[17] RAMZAN F, KHAN M U G, REHMAT A, et al. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks[J]. J Med Syst, 2019, 44(2): 37.
[18] MAYO C D, GARCIA-BARRERA M A, MAZEROLLE E L, et al. Relationship between DTI metrics and cognitive function in Alzheimer’s disease[J].Front Aging Neurosci, 2019, 10:436.
[19] WEE C Y, YAP P T, LI W, et al. Enriched white matter connectivity networks for accurate identification of MCI patients[J]. Neuroimage, 2011, 54(3): 1812-1822.
[20] MARZBAN E N, ELDEIB A M, YASSINE I A, et al. Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks[J]. PLoS One, 2020, 15(3): e0230409.
[21] JACK C R Jr, ANDREWS S J, BEACH T G, et al. Revised criteria for the diagnosis and staging of Alzheimer’s disease[J]. Nat Med, 2024, 30(8): 2121-2124.
[22] SUPPIAH S, DIDIER M A,VINJAMURI S. The who, when, why, and how of PET amyloid imaging in management of Alzheimer’s disease—review of literature and interesting images[J]. Diagnostics, 2019, 9(2): 65.
[23] PAN X, ADEL M, FOSSATI C, et al. Multilevel feature representation of FDG-PET brain images for diagnosing Alzheimer’s disease[J]. IEEE J Biomed Health Inform, 2019, 23(4): 1499-1506.
[24] CHAVES R, RAMÍREZ J, GÓRRIZ J M. Integrating discretization and association rule-based classification for Alzheimer’s disease diagnosis[J]. Expert Syst Appl, 2013, 40(5): 1571-1578.
[25] HOJJATI S H, EBRAHIMZADEH A, KHAZAEE A, et al. Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI[J]. Comput Biol Med, 2018, 102: 30-39.
[26] ZHU X, SUKH I, SHEN D. A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis[J]. Neuroimage, 2014, 100: 91-105.
[27] JAMES C, RANSON J M, EVERSON R, et al. Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients[J]. JAMA Netw Open, 2021, 4(12): e2136553.
[28] WANG X, JIAO B, LIU H, et al. Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer’s disease[J]. CNS Neurosci Ther, 2022, 28(12): 2206-2217.
[29] BADER J M, GEYER P E, MÜLLER J B, et al. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer’s disease[J]. Mol Syst Biol, 2020, 16(6): e9356.
[30] GIORGIO J, JAGUST W J, BAKER S, et al. A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation[J]. Nat Commun, 2022, 13(1): 1887.
[31] 殷潇潇,王思文,王贺,等.语音重复任务在轻度认知功能障碍检测中的应用[J]. 中国神经精神疾病杂志, 2024, 50(4):247-251.
[32] RODRIGUEZ S, HUG C, TODOROV P, et al. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease[J]. Nat Commun, 2021, 12(1): 1033.
[33] XIE C, ZHUANG X X, NIU Z, et al. Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow[J]. Nat Biomed Eng, 2022, 6(1): 76-93.
[34] YANG X, WANG Y, BYRNE R, et al. Concepts of artificial intelligence for computer-assisted drug discovery[J]. Chem Rev, 2019, 119(18): 10520-10594.
[35] QIU Y,CHENG F. Artificial intelligence for drug discovery and development in Alzheimer’s disease[J]. Curr Opin Struct Biol, 2024, 85: 102776.
[36] KALE M, WANKHEDE N, PAWAR R, et al. AI-driven innovations in Alzheimer’s disease: integrating early diagnosis, personalized treatment, and prognostic modelling[J]. Ageing Res Rev, 2024, 101: 102497.
[37] ANGELUCCI F, AI A R, PIENDEL L, et al. Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials[J]. Curr Opin Struct Biol, 2024, 87: 102857.
[38] SHEEHY L, BOUCHARD S, KAKKAR A, et al. Development and initial testing of an artificial intelligence-based virtual reality companion for people living with dementia in long-term care[J]. J Clin Med, 2024, 13(18): 5574.
[39] RASHID N L A, LEOW Y, KLAININ-YOBAS P, et al. The effectiveness of a therapeutic robot, ‘Paro’, on behavioural and psychological symptoms, medication use, total sleep time and sociability in older adults with dementia: a systematic review and meta-analysis[J]. Int J Nurs Stud, 2023, 145: 104530.
[40] LIVINGSTON G, HUNTLEY J, LIU K Y, et al. Dementia prevention, intervention, and care: 2024 report of the lancet standing commission[J]. Lancet, 2024, 404(10452): 572-628.