A Computational Model of Quantitatively Measuring the Alzheimer's Disease Progression in Face Identification

Yuxuan Zhang

Abstract


There are numerous large-scale biomedical and pharmacological research projects to study Alzheimer’s Disease (AD), and potential drugs and therapeutic interventions to improve this severe disease. Of significant importance are life quality of AD patients.

In particular, AD patient’s ability to recognize intimate family members and nurses’ faces largely decides their life quality. The broad objective of this research is focused on providing methods to determine the extent of disease progress from the viewpoint of recovering as much cognitive ability as possible.

Specifically, this research would computerize the AD patient’s diseased brain and retrained the brain with focus on recovering the visual recognition ability of family member and medical care personnel. Likewise, potential recommendations for the patients’ family members and others who interact with the patients, in order to help improve quality of life and daily interactions.


Keywords


Computational Model; Alzheimer’s Disease; Face Identification

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References


https://www.alz.org/alzheimers-dementia/stages Alzheimer’s Association.

https://www.fluidic.com/resources/researching-therapies-for-alzheimers-disease/ Fluidic Analytics.

Lawton MP. Quality of life in Alzheimer disease. Alzheimer Dis Assoc Disord. 1994; 8 Suppl 3 138-150. PMID: 7999340.

Liu S, Liu S, Cai W, et al. Early diagnosis of alzheimer's disease with deep learning. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, 2014, p. 1015-1018.

Ding Y, Sohn JH, Kawczynski MG, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 2018; 290(2): 456-464. https://doi.org/10.1148/radiol.2018180958

Leopold DA, Rhodes G. A comparative view of face perception. J Comp Psychol. 2010; 124: 233–251. https://doi.org/10.1037/a0019460.

Cootes TF, Taylor CJ, Cooper DH, et al. Active shape models-their training and application. Computer Vision and Image Understanding Volume 1995; 61(1): 38-59.

https://doi.org/10.1006/cviu.1995.1004.

Viola P, Jones MJ. Robust real-time face detection. International Journal of Computer Vision 2004; 57: 137. https://doi.org/10.1023/B:VISI.0000013087.49260.fb.

Turk M, Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience 1991; (3)1: 71-86.

http://dx.doi.org/10.1162/jocn.1991.3.1.71.

Sirovich L, Kirby M. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A 1987; 4(3): 519–524. https://doi.org/10.1364/JOSAA.4.000519

Perlibakas V. Distance measures for PCA-based face recognition. Pattern Recognition Letters 2004; (25) 6: 711-724.

https://doi.org/10.1016/j.patrec.2004.01.011.

Tales T, Haworth J, Wilcock G et al. Visual mismatch negativity highlights abnormal pre-attentive visual processing in mild cognitive impairment and Alzheimer's disease. Neuropsychologia 2008; 46(5): 8, Pages 1224-1232. https://doi.org/10.1016/j.neuropsychologia.2007.11.017.

Lopis D, Baltazar M, Geronikola N. et al. Eye contact effects on social preference and face recognition in normal ageing and in Alzheimer’s disease. Psychological Research. 2019; 83: 1292. https://doi.org/10.1007/s00426-017-0955-6.

Alichniewicz K, Brunner F, Klünemann HH, et al. Neural correlates of saccadic inhibition in healthy elderly and patients with amnestic mild cognitive impairment. Front. Psychol 2013; 4: 467. https://doi.org/10.3389/fpsyg.2013.00467.

Firestone A, Turk-Browne NB, Ryan JD. Age-related deficits in face recognition are related to underlying changes in scanning behavior. Aging, Neuropsychology, and Cognition 2007; 14(6): 594-607. https://doi.org/10.1080/13825580600899717.




DOI: http://dx.doi.org/10.18686/esta.v6i1.93

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