Research on the application of convolution neural network in the diagnosis of Alzheimer’s disease_Journal of Biomedical Engineering

Authors：

XU Baohong ^1,2 ,  DING Chong ^1,2 , XU Guizhi ^1,2

1. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China;
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding?author：

Keywords：

Alzheimer's disease; mild cognitive impairment; convolutional neural network; brain imaging

DOI：

10.7507/1001-5515.202007019

Video：

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With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer’s disease, and discusses the existing problems and gives the possible development directions in order to provide some references.

Citation： XU Baohong, DING Chong, XU Guizhi. Research on the application of convolution neural network in the diagnosis of Alzheimer’s disease. Journal of Biomedical Engineering, 2021, 38(1): 169-177, 184. doi: 10.7507/1001-5515.202007019 Copy

1.	Alzheimer’s Disease International Association. The state of the art of dementia research: New frontiers. London: Alzheimer's Disease International Association, 2018.
2.	Alzheimer’s Disease International Association. Attitudes to dementia. London: Alzheimer's Disease International Association, 2019.
3.	Wu C, Guo S, Hong Y, et al. Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant Imaging Med Surg, 2018, 8(10): 992-1003.
4.	Leandrou S, Petroudi S, Kyriacou P A, et al. Quantitative MRI brain studies in mild cognitive impairment and Alzheimer's disease: a methodological review. IEEE Rev Biomed Eng, 2018, 11: 97-111.
5.	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: Clinical, 2018, 21: 1-8.
6.	Spasov S, Passamonti L, Duggento A, et al. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage, 2019, 189: 276-287.
7.	Alzheimer’s Association. 2018 Alzheimer's disease facts and figures. Alzheimers Dement, 2019, 14(3): 5-15.
8.	陳華, 圣文. 老年癡呆患者神經心理特點及影像學表現分析. 中國健康心理學雜志, 2019, 27(4): 25-28.
9.	Nadebaum D P, Krishnadas N, Poon AM, et al. A head-to-head comparison of cerebral blood flow SPECT and 18 F-FDG PET in the diagnosis of Alzheimer's disease. Intern Med J, 2020. DOI: 10.1111/imj.14890.
10.	呂惟群, 曹靜, 王彩榮, 等. CT和MRI診斷阿爾茨海默癥的對照研究. 中國醫療設備, 2019, 34(S2): 101-102.
11.	Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 1962, 160(1): 106-154.
12.	Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
13.	LeCun Y, Boser B, Denker J, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
14.	LeCun Y, Bottou L. Gradient-based learning applied to document recognition. Proc IEEE Inst Electr Electron Eng, 1998, 86(11): 2278-2324.
15.	夏維亞. 基于SPM的腦功能磁共振圖像頭動校正算法研究. 科教導刊, 2018(15): 54-55.
16.	Oschmann M, Gawryluk J R, Alzheimer's Disease Neuroimaging Initiative. A longitudinal study of changes in resting-state functional magnetic resonance imaging functional connectivity networks during healthy aging. Brain Connect, 2020, 10(7): 377-384.
17.	Sarraf S, Tofighi G. Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data// 2016 Future Technologies Conference (FTC). San Francisco: IEEE, 2016: 816-820.
18.	Dai Y, Qiu D, Wang Y, et al. Research on computer-aided diagnosis of Alzheimer's disease based on heterogeneous medical data fusion. Int J Patt Recogn Artif Intell, 2019, 33(5): 1957001.1-1957001.17.
19.	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
20.	Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15: 1929-1958.
21.	Shakarami A, Tarrah H, Mahdavi-Hormat A. A CAD system for diagnosing Alzheimer's disease using 2D slices and an improved AlexNet-SVM method. Optik, 2020, 212: 164237.
22.	Lee B, Ellahi W, Choi J Y. Using deep CNN with data permutation scheme for classification of Alzheimer's disease in structural magnetic resonance imaging (sMRI). IEICE Trans Inf Syst, 2019, E102-D(7): 1384-1395.
23.	Kazemi Y, Houghten S. A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data// 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). St. Louis: IEEE, 2018: 1-8.
24.	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 1-9.
25.	Ding Y, Sohn J H, Kawczynski M G, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using (18)F-FDG PET of the brain. Radiology, 2019, 290(2): 456-464.
26.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition// International Conference on Learning Representations. San Diego: ICLR, 2015: 1-14.
27.	Mehmood A, Maqsood M, Bashir M, et al. A deep siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci, 2020, 10(2): 1-15.
28.	Jiang J W, Li K, Huang J J, et al. Deep learning based mild cognitive impairment diagnosis using structure MR images. Neurosci Lett, 2020, 730: 134971.
29.	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.
30.	Khan N M, Abraham N, Hon M. Transfer learning with intelligent training data selection for prediction of Alzheimer's disease. IEEE Access, 2019, 7: 72726-72735.
31.	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.
32.	Menikdiwela M, Nguyen C, Shaw M. Deep learning on brain cortical thickness data for disease classification// 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra: DICTA, 2018: 1-5.
33.	Yee E, Popuri K, Beg M F, et al. Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score. Hum Brain Mapp, 2019, 41(1): 5-16.
34.	Fulton L V, Dolezel D, Harrop J, et al. Classification of Alzheimer's disease with and without imagery using gradient boosted machines and ResNet-50. Brain Sci, 2019, 9(9): 1-16.
35.	Pleiss G, Chen D, Huang G, et al. Memory-efficient implementation of DenseNets. arXiv, 2017: 1707.06990v1[csCV].
36.	Li H, He X, Zhou F, et al. Dense deconvolutional network for skin lesion segmentation. IEEE J Biomed Health Inform, 2019, 23(2): 527-537.
37.	黃奕暉, 馮前進. 基于三維全卷積DenseNet的腦膠質瘤MRI分割. 南方醫科大學學報, 2018, 38(6): 661-668.
38.	Huang W, Feng J J, Wang H, et al. A new architecture of densely connected convolutional networks for pan-sharpening. ISPRS Int J Geo-Inf, 2020, 9(4): 242-262.
39.	Wang H, Shen Y, Wang S, et al. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 2019, 333: 145-156.
40.	Li F, Liu M. A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer's disease. J Neurosci Methods, 2019, 323: 108-118.
41.	Backstrom K, Nazari M, Gu I Y, et al. An efficient 3D deep convolutional network for Aleheimer's disease diagnosis using MR images// 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington: IEEE, 2018: 149-153.
42.	Jiang X W, Chang L, Zhang Y D. Classification of Alzheimer's disease via eight-layer convolutional neural network with batch normalization and dropout techniques. J Med Imaging Health Inform, 2020, 10(5): 1040-1048.
43.	Afzal S, Maqsood M, Nazir F, et al. A data augmentation-based framework to handle class imbalance problem for Alzheimer's stage detection. IEEE Access, 2019, 7: 115528-115539.
44.	Fang X, Liu Z, Xu M. Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis. IET Image Processing, 2020, 14(2): 318-326.
45.	Chicco D. Siamese neural networks: an overview. Methods Mol Biol, 2021, 2190: 73-94.
46.	Liu C F, Padhy S, Ramachandran S, et al. Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment. Magn Reson Imaging, 2019, 64: 190-199.
47.	Prakash D, Madusanka N, Bhattacharjee S, et al. A comparative study of Alzheimer’s disease classification using multiple transfer learning models. J Multimed Inf Syst, 2019, 6(4): 209-216.
48.	Kwon G-R, Khagi B. CNN model performance analysis on MRI images of an OASIS dataset for distinction between healthy and Alzheimer’s patients. IEIE Trans Smart Process Comput, 2019, 8(4): 272-278.
49.	Chaddad A, Desrosiers C, Niazi T. Deep radiomic analysis of MRI related to Alzheimer's disease. IEEE Access, 2018, 6: 58213-58221.
50.	Nawaz H, Maqsood M, Afzal S, et al. A deep feature-based real-time system for Alzheimer disease stage detection. Multimed Tools Appl, 2020(4): 1-19.
51.	Duc N T, Ryu S, Qureshi M N I, et al. 3D-Deep learning based automatic diagnosis of Alzheimer's disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics, 2019, 18(1): 71-86.
52.	Amoroso N, Diacono D, Fanizzi A, et al. Deep learning reveals Alzheimer's disease onset in MCI subjects: results from an international challenge. J Neurosci Methods, 2018, 302: 3-9.
53.	Zhang F, Li Z, Zhang B Y, et al. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease. Neurocomputing, 2019, 361: 185-195.
54.	Marzban E N, Eldeib A M, Yassine I A, et al. Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks. PLoS One, 2020, 15(3): 1-16.
55.	Cho S, Choi J. Visualization of convolutional neural networks for time series input data. J KIISE, 2020, 47(5): 445-453.
56.	Yang C, Rangarajan A, Ranka S. Visual explanations from deep 3D convolutional neural networks for Alzheimer's disease classification. AMIA Annual Symposium proceedings. AMIA Symposium, 2018, 2018: 1571-1580.
57.	Jiang P, Wang X, Li Q, et al. Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of Alzheimer's disease. IEEE J Biomed Health Inform, 2019, 23(4): 1450-1456.
58.	Liu X, Cao P, Wang J, et al. Fused group lasso regularized multi-task feature learning and its application to the cognitive performance prediction of Alzheimer's disease. Neuroinformatics, 2019, 17(2): 271-294.
59.	Wang M, Zhang D, Shen D, et al. Multi-task exclusive relationship learning for Alzheimer's disease progression prediction with longitudinal data. Med Image Anal, 2019, 53: 111-122.
60.	Lian C, Liu M, Pan Y, et al. Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern, 2020: 1-12.

1. Alzheimer’s Disease International Association. The state of the art of dementia research: New frontiers. London: Alzheimer's Disease International Association, 2018.
2. Alzheimer’s Disease International Association. Attitudes to dementia. London: Alzheimer's Disease International Association, 2019.
3. Wu C, Guo S, Hong Y, et al. Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant Imaging Med Surg, 2018, 8(10): 992-1003.
4. Leandrou S, Petroudi S, Kyriacou P A, et al. Quantitative MRI brain studies in mild cognitive impairment and Alzheimer's disease: a methodological review. IEEE Rev Biomed Eng, 2018, 11: 97-111.
5. 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: Clinical, 2018, 21: 1-8.
6. Spasov S, Passamonti L, Duggento A, et al. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage, 2019, 189: 276-287.
7. Alzheimer’s Association. 2018 Alzheimer's disease facts and figures. Alzheimers Dement, 2019, 14(3): 5-15.
8. 陳華, 圣文. 老年癡呆患者神經心理特點及影像學表現分析. 中國健康心理學雜志, 2019, 27(4): 25-28.
9. Nadebaum D P, Krishnadas N, Poon AM, et al. A head-to-head comparison of cerebral blood flow SPECT and 18 F-FDG PET in the diagnosis of Alzheimer's disease. Intern Med J, 2020. DOI: 10.1111/imj.14890.
10. 呂惟群, 曹靜, 王彩榮, 等. CT和MRI診斷阿爾茨海默癥的對照研究. 中國醫療設備, 2019, 34(S2): 101-102.
11. Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol, 1962, 160(1): 106-154.
12. Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
13. LeCun Y, Boser B, Denker J, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
14. LeCun Y, Bottou L. Gradient-based learning applied to document recognition. Proc IEEE Inst Electr Electron Eng, 1998, 86(11): 2278-2324.
15. 夏維亞. 基于SPM的腦功能磁共振圖像頭動校正算法研究. 科教導刊, 2018(15): 54-55.
16. Oschmann M, Gawryluk J R, Alzheimer's Disease Neuroimaging Initiative. A longitudinal study of changes in resting-state functional magnetic resonance imaging functional connectivity networks during healthy aging. Brain Connect, 2020, 10(7): 377-384.
17. Sarraf S, Tofighi G. Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data// 2016 Future Technologies Conference (FTC). San Francisco: IEEE, 2016: 816-820.
18. Dai Y, Qiu D, Wang Y, et al. Research on computer-aided diagnosis of Alzheimer's disease based on heterogeneous medical data fusion. Int J Patt Recogn Artif Intell, 2019, 33(5): 1957001.1-1957001.17.
19. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
20. Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15: 1929-1958.
21. Shakarami A, Tarrah H, Mahdavi-Hormat A. A CAD system for diagnosing Alzheimer's disease using 2D slices and an improved AlexNet-SVM method. Optik, 2020, 212: 164237.
22. Lee B, Ellahi W, Choi J Y. Using deep CNN with data permutation scheme for classification of Alzheimer's disease in structural magnetic resonance imaging (sMRI). IEICE Trans Inf Syst, 2019, E102-D(7): 1384-1395.
23. Kazemi Y, Houghten S. A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data// 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). St. Louis: IEEE, 2018: 1-8.
24. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 1-9.
25. Ding Y, Sohn J H, Kawczynski M G, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using (18)F-FDG PET of the brain. Radiology, 2019, 290(2): 456-464.
26. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition// International Conference on Learning Representations. San Diego: ICLR, 2015: 1-14.
27. Mehmood A, Maqsood M, Bashir M, et al. A deep siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci, 2020, 10(2): 1-15.
28. Jiang J W, Li K, Huang J J, et al. Deep learning based mild cognitive impairment diagnosis using structure MR images. Neurosci Lett, 2020, 730: 134971.
29. 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.
30. Khan N M, Abraham N, Hon M. Transfer learning with intelligent training data selection for prediction of Alzheimer's disease. IEEE Access, 2019, 7: 72726-72735.
31. 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.
32. Menikdiwela M, Nguyen C, Shaw M. Deep learning on brain cortical thickness data for disease classification// 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra: DICTA, 2018: 1-5.
33. Yee E, Popuri K, Beg M F, et al. Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score. Hum Brain Mapp, 2019, 41(1): 5-16.
34. Fulton L V, Dolezel D, Harrop J, et al. Classification of Alzheimer's disease with and without imagery using gradient boosted machines and ResNet-50. Brain Sci, 2019, 9(9): 1-16.
35. Pleiss G, Chen D, Huang G, et al. Memory-efficient implementation of DenseNets. arXiv, 2017: 1707.06990v1[csCV].
36. Li H, He X, Zhou F, et al. Dense deconvolutional network for skin lesion segmentation. IEEE J Biomed Health Inform, 2019, 23(2): 527-537.
37. 黃奕暉, 馮前進. 基于三維全卷積DenseNet的腦膠質瘤MRI分割. 南方醫科大學學報, 2018, 38(6): 661-668.
38. Huang W, Feng J J, Wang H, et al. A new architecture of densely connected convolutional networks for pan-sharpening. ISPRS Int J Geo-Inf, 2020, 9(4): 242-262.
39. Wang H, Shen Y, Wang S, et al. Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 2019, 333: 145-156.
40. Li F, Liu M. A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer's disease. J Neurosci Methods, 2019, 323: 108-118.
41. Backstrom K, Nazari M, Gu I Y, et al. An efficient 3D deep convolutional network for Aleheimer's disease diagnosis using MR images// 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington: IEEE, 2018: 149-153.
42. Jiang X W, Chang L, Zhang Y D. Classification of Alzheimer's disease via eight-layer convolutional neural network with batch normalization and dropout techniques. J Med Imaging Health Inform, 2020, 10(5): 1040-1048.
43. Afzal S, Maqsood M, Nazir F, et al. A data augmentation-based framework to handle class imbalance problem for Alzheimer's stage detection. IEEE Access, 2019, 7: 115528-115539.
44. Fang X, Liu Z, Xu M. Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis. IET Image Processing, 2020, 14(2): 318-326.
45. Chicco D. Siamese neural networks: an overview. Methods Mol Biol, 2021, 2190: 73-94.
46. Liu C F, Padhy S, Ramachandran S, et al. Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment. Magn Reson Imaging, 2019, 64: 190-199.
47. Prakash D, Madusanka N, Bhattacharjee S, et al. A comparative study of Alzheimer’s disease classification using multiple transfer learning models. J Multimed Inf Syst, 2019, 6(4): 209-216.
48. Kwon G-R, Khagi B. CNN model performance analysis on MRI images of an OASIS dataset for distinction between healthy and Alzheimer’s patients. IEIE Trans Smart Process Comput, 2019, 8(4): 272-278.
49. Chaddad A, Desrosiers C, Niazi T. Deep radiomic analysis of MRI related to Alzheimer's disease. IEEE Access, 2018, 6: 58213-58221.
50. Nawaz H, Maqsood M, Afzal S, et al. A deep feature-based real-time system for Alzheimer disease stage detection. Multimed Tools Appl, 2020(4): 1-19.
51. Duc N T, Ryu S, Qureshi M N I, et al. 3D-Deep learning based automatic diagnosis of Alzheimer's disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics, 2019, 18(1): 71-86.
52. Amoroso N, Diacono D, Fanizzi A, et al. Deep learning reveals Alzheimer's disease onset in MCI subjects: results from an international challenge. J Neurosci Methods, 2018, 302: 3-9.
53. Zhang F, Li Z, Zhang B Y, et al. Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease. Neurocomputing, 2019, 361: 185-195.
54. Marzban E N, Eldeib A M, Yassine I A, et al. Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks. PLoS One, 2020, 15(3): 1-16.
55. Cho S, Choi J. Visualization of convolutional neural networks for time series input data. J KIISE, 2020, 47(5): 445-453.
56. Yang C, Rangarajan A, Ranka S. Visual explanations from deep 3D convolutional neural networks for Alzheimer's disease classification. AMIA Annual Symposium proceedings. AMIA Symposium, 2018, 2018: 1571-1580.
57. Jiang P, Wang X, Li Q, et al. Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of Alzheimer's disease. IEEE J Biomed Health Inform, 2019, 23(4): 1450-1456.
58. Liu X, Cao P, Wang J, et al. Fused group lasso regularized multi-task feature learning and its application to the cognitive performance prediction of Alzheimer's disease. Neuroinformatics, 2019, 17(2): 271-294.
59. Wang M, Zhang D, Shen D, et al. Multi-task exclusive relationship learning for Alzheimer's disease progression prediction with longitudinal data. Med Image Anal, 2019, 53: 111-122.
60. Lian C, Liu M, Pan Y, et al. Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE Trans Cybern, 2020: 1-12.

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