An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion_Journal of Biomedical Engineering

Authors：

ZENG An ¹ , WANG Jianbin ¹ ,  PAN Dan ² , YANG Yang ³ , LIU Jun ⁴ , LIU Xin ¹ , CHEN Wenge ⁵ , WU Juhua ⁵

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, P. R. China;
2. School of Electronics and Information Engineering, Guangdong University of Technology and Education, Guangzhou 510665, P. R. China;
3. Information Management Department, Guangdong Provincial People's Hospital, Guangzhou 510080, P. R. China;
4. Neurology Department, Affiliated Second Hospital of Guangzhou Medical University, Guangzhou 510260, P. R. China;
5. School of Management, Guangdong University of Technology, Guangzhou 510006, P. R. China;

Corresponding author：

PAN Dan, Email: pandan@gpnu.edu.cn

Keywords：

Alzheimer's disease; Three-dimensional convolutional neural network; Twin neural network; Multimodal data

DOI：

10.7507/1001-5515.202310046

Video：

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Abstract Full text Figures/Tables Video References Cited by

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Citation： ZENG An, WANG Jianbin, PAN Dan, YANG Yang, LIU Jun, LIU Xin, CHEN Wenge, WU Juhua. An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion. Journal of Biomedical Engineering, 2024, 41(3): 485-493. doi: 10.7507/1001-5515.202310046 Copy

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21.	Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev, 2016, 30: 25-48.
22.	Pan D, Zeng A, Yang B, et al. Deep learning for brain MRI confirms patterned pathological progression in Alzheimer's disease. Adv Sci (Weinh), 2023, 10(6): e2204717.
23.	Fan L, Li H, Zhuo J, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex, 2016, 26(8): 3508-3526.
24.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016: 770-778.
25.	Pan D, Luo G, Zeng A, et al. Adaptive 3DCNN-based interpretable ensemble model for early diagnosis of Alzheimer's disease. IEEE Transactions on Computational Social Systems, 2024, 11(1): 247-266.
26.	Touvron H, Cord M, Douze M, et al. Training data-efficient image transformers & distillation through attention. arXiv preprint, 2021, arXiv: 2012.12877.

1. Alzheimer's Disease International. World Alzheimer report 2023. (2023-09-21) [2024-03-29]. https://www.alzint.org/resource/world-alzheimer-report-2023/.
2. Ballard C, Gauthier S, Corbett A, et al. Alzheimer's disease. Lancet, 2011, 377(9768): 629-638.
3. Dubois B, Feldman H H, Jacova C, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol, 2007, 6(8): 734-746.
4. 罗强, 罗雅楠, 冯娜娜, 等. 阿尔茨海默病早期诊断技术前沿概述. 中国公共卫生, 2023, 39(3): 394-399.
5. Jack C R, Albert M S, Knopman D S, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia, 2011, 7(3): 257-262.
6. 蔡丽娜, 李晓陵, 潘洋, 等. MRI影像组学在轻度认知障碍中的研究进展. 磁共振成像, 2022, 13(6): 131-134.
7. Zhao X, Ang C K E, Acharya U R, et al. Application of artificial intelligence techniques for the detection of Alzheimer's disease using structural MRI images. Biocybernetics and Biomedical Engineering, 2021, 41(2): 456-473.
8. Payan A, Montana G. Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint, 2015, arXiv: 1502.02506.
9. Gupta A, Ayhan M S, Maida A S. Natural image bases to represent neuroimaging data//Proceedings of the 30^th International Conference on Machine Learning (ICML’13), Atlanta: JMLR, 2013: 987-994.
10. Wang S H, Phillips P, Sui Y, et al. Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst, 2018, 42(5): 85.
11. Jain R, Jain N, Aggarwal A, et al. Convolutional neural network based Alzheimer's disease classification from magnetic resonance brain images. Cogn Syst Res, 2019, 57: 147-159.
12. Basaia S, Agosta F, Wagner L, et al. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin, 2019, 21: 101645.
13. Oh K, Chung Y C, Kim K W, et al. Classification and visualization of Alzheimer's disease using volumetric convolutional neural network and transfer learning. Sci Rep, 2019, 9(1): 18150.
14. Liu S, Yadav C, Fernandez-Granda C, et al. On the design of convolutional neural networks for automatic detection of Alzheimer's disease//Proceedings of the Machine Learning for Health NerIPS Workshop, PMLR, 2020, 116: 184-201.
15. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint, 2020, arXiv: 2010.11929.
16. Chen J, He Y, Frey E C, et al. ViT-V-net: vision transformer for unsupervised volumetric medical image registration. arXiv preprint, 2021, arXiv: 2104.06468.
17. Kushol R, Masoumzadeh A, Huo D, et al. Addformer: Alzheimer's disease detection from structural MRI using fusion transformer//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India: IEEE, 2022: 1-5.
18. Hoang G M, Kim U H, Kim J G. Vision transformers for the prediction of mild cognitive impairment to Alzheimer's disease progression using mid-sagittal sMRI. Frontiers in Aging Neuroscience, 2023, 15: 1102869.
19. Cui R, Liu M, Gang L. Longitudinal analysis for Alzheimer's disease diagnosis using RNN//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington: IEEE, 2018: 1398-1401.
20. Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu: IEEE, 2017: 2261-2269.
21. Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev, 2016, 30: 25-48.
22. Pan D, Zeng A, Yang B, et al. Deep learning for brain MRI confirms patterned pathological progression in Alzheimer's disease. Adv Sci (Weinh), 2023, 10(6): e2204717.
23. Fan L, Li H, Zhuo J, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex, 2016, 26(8): 3508-3526.
24. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016: 770-778.
25. Pan D, Luo G, Zeng A, et al. Adaptive 3DCNN-based interpretable ensemble model for early diagnosis of Alzheimer's disease. IEEE Transactions on Computational Social Systems, 2024, 11(1): 247-266.
26. Touvron H, Cord M, Douze M, et al. Training data-efficient image transformers & distillation through attention. arXiv preprint, 2021, arXiv: 2012.12877.

Journal of Biomedical Engineering

An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion

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