Interpretable Machine Learning for Dementia: A Systematic Review

Overview

Journal Alzheimers Dement

Specialties Neurology
Psychiatry

Date 2023 Feb 3

PMID 36735865

Authors

Sophie A Martin

Florence J Townend

Frederik Barkhof

James H Cole

Affiliations

Soon will be listed here.

Abstract

Introduction: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models.

Methods: Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia.

Results: We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets.

Discussion: Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.

Citing Articles

Use machine learning to predict treatment outcome of early childhood caries.

Wu Y, Jia M, Fang Y, Duangthip D, Chu C, Gao S BMC Oral Health. 2025; 25(1):389.

PMID: 40089762 DOI: 10.1186/s12903-025-05768-y.

A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.

Vlontzou M, Athanasiou M, Dalakleidi K, Skampardoni I, Davatzikos C, Nikita K Sci Rep. 2025; 15(1):8410.

PMID: 40069342 PMC: 11897299. DOI: 10.1038/s41598-025-92577-6.

Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.

Gu Z, Liu S, Ma H, Long Y, Jiao X, Gao X JMIR Aging. 2025; 8:e64148.

PMID: 40009844 PMC: 11904384. DOI: 10.2196/64148.

Cognitive performance classification of older patients using machine learning and electronic medical records.

Richter-Laskowska M, Sobotnicka E, Bednorz A Sci Rep. 2025; 15(1):6564.

PMID: 39994339 PMC: 11850844. DOI: 10.1038/s41598-025-90460-y.

Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging - A systematic review and meta-analysis.

Wu Q, Kiakou D, Mueller K, Kohler W, Schroeter M Neuroimage Clin. 2025; 45:103757.

PMID: 39983552 PMC: 11889731. DOI: 10.1016/j.nicl.2025.103757.

References

Das D, Ito J, Kadowaki T, Tsuda K . An interpretable machine learning model for diagnosis of Alzheimer's disease. PeerJ. 2019; 7:e6543. PMC: 6398390. DOI: 10.7717/peerj.6543. View

Rudin C . Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2022; 1(5):206-215. PMC: 9122117. DOI: 10.1038/s42256-019-0048-x. View

Bohle M, Eitel F, Weygandt M, Ritter K . Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification. Front Aging Neurosci. 2019; 11:194. PMC: 6685087. DOI: 10.3389/fnagi.2019.00194. View

Weiner M, Veitch D, Aisen P, Beckett L, Cairns N, Cedarbaum J . Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement. 2015; 11(7):865-84. PMC: 4659407. DOI: 10.1016/j.jalz.2015.04.005. View

Page M, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021; 10(1):89. PMC: 8008539. DOI: 10.1186/s13643-021-01626-4. View

Tjoa E, Guan C . A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Trans Neural Netw Learn Syst. 2020; 32(11):4793-4813. DOI: 10.1109/TNNLS.2020.3027314. View

Liu Z, Adeli E, Pohl K, Zhao Q . Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models. Inf Process Med Imaging. 2021; 12729:71-82. PMC: 8451265. DOI: 10.1007/978-3-030-78191-0_6. View

Qiu S, Joshi P, Miller M, Xue C, Zhou X, Karjadi C . Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain. 2020; 143(6):1920-1933. PMC: 7296847. DOI: 10.1093/brain/awaa137. View

Iizuka T, Fukasawa M, Kameyama M . Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Sci Rep. 2019; 9(1):8944. PMC: 6586613. DOI: 10.1038/s41598-019-45415-5. View

10.

Salahuddin Z, Woodruff H, Chatterjee A, Lambin P . Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med. 2021; 140:105111. DOI: 10.1016/j.compbiomed.2021.105111. View

11.

Oxtoby N, Garbarino S, Firth N, Warren J, Schott J, Alexander D . Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease. Front Neurol. 2017; 8:580. PMC: 5681907. DOI: 10.3389/fneur.2017.00580. View

12.

Orlenko A, Moore J . A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions. BioData Min. 2021; 14(1):9. PMC: 7847145. DOI: 10.1186/s13040-021-00243-0. View

13.

Varoquaux G . Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage. 2017; 180(Pt A):68-77. DOI: 10.1016/j.neuroimage.2017.06.061. View

14.

Cabral C, Morgado P, Campos Costa D, Silveira M . Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med. 2015; 58:101-9. DOI: 10.1016/j.compbiomed.2015.01.003. View

15.

Sengupta P, Chandrashekhar Y . Building Trust in AI: Opportunities and Challenges for Cardiac Imaging. JACC Cardiovasc Imaging. 2021; 14(2):520-522. DOI: 10.1016/j.jcmg.2021.01.002. View

16.

Liu Y, Li Z, Ge Q, Lin N, Xiong M . Deep Feature Selection and Causal Analysis of Alzheimer's Disease. Front Neurosci. 2019; 13:1198. PMC: 6872503. DOI: 10.3389/fnins.2019.01198. View

17.

Petersen R, Aisen P, Beckett L, Donohue M, Gamst A, Harvey D . Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010; 74(3):201-9. PMC: 2809036. DOI: 10.1212/WNL.0b013e3181cb3e25. View

18.

Weiner M, Aisen P, Jack Jr C, Jagust W, Trojanowski J, Shaw L . The Alzheimer's disease neuroimaging initiative: progress report and future plans. Alzheimers Dement. 2010; 6(3):202-11.e7. PMC: 2927112. DOI: 10.1016/j.jalz.2010.03.007. View

19.

Ding Y, Sohn J, Kawczynski M, Trivedi H, Harnish R, Jenkins N . A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain. Radiology. 2018; 290(2):456-464. PMC: 6358051. DOI: 10.1148/radiol.2018180958. View

20.

Mongan J, Moy L, Kahn Jr C . Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2021; 2(2):e200029. PMC: 8017414. DOI: 10.1148/ryai.2020200029. View