Artificial Intelligence’s Aid in Diagnosing Alzheimer’s Disease


  • Prisha Thoguluva Central Jersey College Prep (CJCP) Charter School
  • Dr. Rajagopalan Appavu University of South Florida



Healthcare, Medicine, Artificial Intelligence, AI, Machine Learning, ML, Alzheimer's, Neurology, Brain, Cognitive, Imaging, Deep Learning, CNN


Alzheimer’s disease (AD) is a brain disorder that gradually destroys memory and thinking skills and is the most common cause of dementia among older adults. AD results from the progressive degeneration of brain cells and can affect the ability of people to carry out simple tasks. AD is extremely hard for clinicians to detect and understand because an accurate diagnosis of the disease is possible only through an autopsy after the death of an affected patient. Artificial Intelligence, referring to the ability of a computer to simulate human tasks, shows great potential in helping clinicians better understand a patient's brain condition and spot and analyze brain deformity. This paper explores how Artificial Intelligence can revolutionize Alzheimer’s disease diagnosis and proposes a diagnosis roadmap for doctors to use when assessing a patient’s brain health.


Download data is not yet available.

Author Biography

Dr. Rajagopalan Appavu, University of South Florida


References or Bibliography


Artificial Intelligence In Healthcare Market Report, 2022–2030. (2022). Artificial Intelligence In Healthcare Market Size (2022 - 2030).

Boyle, A. J., Gaudet, V. C., Black, S. E., Vasdev, N., Rosa-Neto, P., & Zukotynski, K. A. (2021). Artificial intelligence for molecular neuroimaging. Annals of Translational Medicine, 9(9), 822.

Byun, M.S., Yi, D., Lee, J., Choe, Y.M., Sohn, B.K., Lee, J., Choi, H.J., Baek, H., Kim, Y., Lee, Y., Sohn, C., Mook‐Jung, I., Choi, M., Lee, Y.J., Lee, D.W., Ryu, S., Kim, S.G., Kim, J.W., Woo, J.I., & Lee, D.Y. (2017). Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease: Methodology and Baseline Sample Characteristics. Psychiatry Investigation, 14, 851 - 863.

Cummings, J. L., Tong, G., & Ballard, C. (2019). Treatment Combinations for Alzheimer’s Disease: Current and Future Pharmacotherapy Options. Journal of Alzheimer’s Disease, 67(3), 779–794.

Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C., & Initiative, A.D. (2008). Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage, 39, 1731-1743.

Farlow, M. R., Miller, M. L., & Pejovic, V. (2008). Treatment Options in Alzheimer’s Disease: Maximizing Benefit, Managing Expectations. Dementia and Geriatric Cognitive Disorders, 25(5), 408–422.

Filipovych, R., & Davatzikos, C. (2011). Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI). NeuroImage, 55, 1109-1119.

Han, X. (2017). Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method. ISBI 2017 LiTS Challenge ISIC 2017, 1–4.

Johnson, P. M., Recht, M. P., & Knoll, F. (2020). Improving the Speed of MRI with Artificial Intelligence. Seminars in Musculoskeletal Radiology, 24(01), 012–020.

Jolliffe, I.T. (1986). Principal Component Analysis for Special Types of Data.

Kurlowicz, L., & Wallace, M. (1999). The Mini Mental State Examination (MMSE). The Hartford Institute for Geriatric Nursing, 3.

Li, F., Tran, L.Q., Thung, K., Ji, S., Shen, D., & Li, J. (2015). A Robust Deep Model for Improved Classification of AD/MCI Patients. IEEE Journal of Biomedical and Health Informatics, 19, 1610-1616.

Marcello, E., Gardoni, F., & di Luca, M. (2015). Alzheimer’s disease and modern lifestyle: what is the role of stress? Journal of Neurochemistry, 134(5), 795–798.

Matsumoto, Y., & Kohyama, K. (2015). Alzheimer’s disease and immunotherapy: what is wrong with clinical trials? ImmunoTargets and Therapy, 27.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (2011). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS--ADRDA Work Group under the auspices of the Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 77(4), 333.

Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194.

Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of neurology, 56 3, 303-8 .

Ray, S. (2021, August 26). SVM | Supporting Vector Machine Algorithm in Machine Learning. Analytics Vidhya.

Sankari, Z., & Adeli, H. (2011). Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence. Journal of Neuroscience Methods, 197, 165-170.

Serengil, S. (2020, May 4). Convolutional Autoencoder: Clustering Images with Neural Networks. Sefik Ilkin Serengil.

Yang, K., & Mohammed, E. (2020). A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer’s Disease. A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer’s Disease, 1–9.

Zhao, B., Wang, F., & Zhang, C. (2008). Cuts3vm: a fast semi-supervised svm algorithm. KDD.

Zhao, Y., Raichle, M.E., Wen, J., Benzinger, T.L., Fagan, A.M., Hassenstab, J., Vlassenko, A.G., Luo, J., Cairns, N.J., Christensen, J.J., Morris, J.C., & Yablonskiy, D.A. (2017). In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging. NeuroImage, 148, 296-304.



How to Cite

Thoguluva, P., & Appavu, D. R. (2022). Artificial Intelligence’s Aid in Diagnosing Alzheimer’s Disease. Journal of Student Research, 11(2).



HS Review Articles