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Volume 1 , Issue 2 , PP: 22-36, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
El-Sayed M. El-kenawy 1 *
Doi: https://doi.org/10.54216/MOR.010203
This review aims to discuss the use of AI and ML in diagnosing and managing neurodegenerative diseases, with particular emphasis on AD and MCI. Emerging innovations present in depth the effectiveness of using ML models such as SVM, random forests, CNNs, and new frameworks such as quantum-classical neural networks on data obtained from MRI imaging, EEG signals, genetic makers and sociodemographic data. Widely used research findings demonstrate that these tools offer seemingly higher detection rates, sensitivity, and specificity than traditional diagnostic techniques for identifying and diagnosing early-stage illnesses. Some of them are techniques based on analyzing EEG time-frequency bands, combining MRI and PET data integration approaches, and creating telemedicine services to overcome geographical barriers. Furthermore, interpretable AI models improve clinical relevance by providing decision and trust support among practitioners. While these achievements are notable, the following limitations need to be noted, thus making it easier to establish the generalizability of the results and ways of using datasets that are free from bias and difficulties associated with applying AI in clinical settings. There are pressing questions regarding patients' rights and privacy, the issue of homogenization and standardization of data, and the distribution and accessibility of AI tools across industries as well as within the same region. More studies should be conducted to expand AI applications, use a more diverse dataset, and promote cooperation between representatives of various fields of science to ensure that technological advancement meets clinical demands. It also includes new methods like Vision Transformers and Quantum Computing Enhanced Deep Learning to overcome diagnostic issues in time-consuming and multi-parametric data analysis. These gaps can be closed with the help of AI and ML to enhance diagnostic accuracy, select the right treatment strategy, and risk assessment for the long-term management of NDs. In conclusion, this review similarly reaffirms how stunning AI's role is in improving future neurodegenerative disease care. For this reason, the deployment process must be done sensibly to enhance the patient's value most appropriately.
Artificial Intelligence , Machine Learning , Neurodegenerative Diseases , Alzheimer&rsquo , s Disease , Mild Cognitive Impairment , Diagnostic Technologies
[1] “2024 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, vol. 20, no. 5, p. 3708, May 2024, doi: 10.1002/ALZ.13809.
[2] “World Alzheimer Report 2024: Global changes in attitudes to dementia,” Sep. 20, 2024. Accessed: Dec. 04, 2024. [Online]. Available: https://www.alzint.org/resource/world-alzheimer-report-2024/
[3] L. K. Huang, Y. C. Kuan, H. W. Lin, and C. J. Hu, “Clinical trials of new drugs for Alzheimer disease: a 2020–2023 update,” J Biomed Sci, vol. 30, no. 1, pp. 1–19, Dec. 2023, doi: 10.1186/S12929-023-00976-6/FIGURES/3.
[4] K. B. Rajan, J. Weuve, L. L. Barnes, E. A. McAninch, R. S. Wilson, and D. A. Evans, “Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020–2060),” Alzheimer’s and Dementia, vol. 17, no. 12, pp. 1966–1975, Dec. 2021, doi: 10.1002/ALZ.12362.
[5] J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: transforming the practice of medicine,” Future Healthc J, vol. 8, no. 2, p. e188, Jul. 2021, doi: 10.7861/FHJ.2021-0095.
[6] N. Srivastava et al., “Advances in artificial intelligence-based technologies for increasing the quality of medical products,” DARU Journal of Pharmaceutical Sciences 2024 33:1, vol. 33, no. 1, pp. 1–21, Nov. 2024, doi: 10.1007/S40199-024-00548-5.
[7] F. Kitsios, M. Kamariotou, A. I. Syngelakis, and M. A. Talias, “Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review,” Applied Sciences 2023, Vol. 13, Page 7479, vol. 13, no. 13, p. 7479, Jun. 2023, doi: 10.3390/APP13137479.
[8] S. S. Oh et al., “A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation.” JMIR Med Inform, vol. 12, no. 1, p. e59396, Nov. 2024, doi: 10.2196/59396.
[9] S. Daniel Rodrigues and P. Miguel Rodrigues, “Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning,” J Biol Methods, vol. 0, no. 0, p. e99010042, Nov. 2024, doi: 10.14440/JBM.2025.0069.
[10] “(PDF) APPLICATION OF ARTIFICIAL INTELLIGENCE IN EARLY DIAGNOSIS OF NEURODEGENERATIVE DISEASES.” Accessed: Dec. 03, 2024. [Online]. Available: https://www.researchgate.net/publication/386099038_APPLICATION_OF_ARTIFICIAL_INTELLIGENCE_IN_EARLY_DIAGNOSIS_OF_NEURODEGENERATIVE_DISEASES
[11] “(PDF) AI Algorithms in Detecting Early Symptoms of Neurological Diseases.” Accessed: Dec. 03, 2024. [Online]. Available: https://www.researchgate.net/publication/386176979_AI_Algorithms_in_Detecting_Early_Symptoms_of_Neurological_Diseases
[12] L. Xu, V. Berisha, J. Liss, S. Jayasuriya, and H. Ghasemzadeh, “Interpretable Analytics for Clinical Applications,” 2024.
[13] M. Alegret et al., “FACEmemory®, an Innovative Self-Administered Online Memory Assessment Tool,” Journal of Clinical Medicine 2024, Vol. 13, Page 7274, vol. 13, no. 23, p. 7274, Nov. 2024, doi: 10.3390/JCM13237274.
[14] V. Guimarães et al., “One-class classification with confound control for cognitive screening in older adults using gait, fingertapping, cognitive, and dual tasks,” Comput Methods Programs Biomed, vol. 259, p. 108508, Feb. 2025, doi: 10.1016/J.CMPB.2024.108508.
[15] M. Sarma and S. Chatterjee, “Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis,” Diagnostics 2024, Vol. 14, Page 2640, vol. 14, no. 23, p. 2640, Nov. 2024, doi: 10.3390/DIAGNOSTICS14232640.
[16] V. Panchal, V. Vyas, A. Jamil, and S. Bin Ahmed, “Classification of Alzheimer’s Disease Stages Using Vision Transformers,” pp. 831–842, 2024, doi: 10.1007/978-3-031-70924-1_63.
[17] M. Abdelaziz, T. Wang, W. Anwaar, and A. Elazab, “Multi-scale multimodal deep learning framework for Alzheimer’s disease diagnosis,” Comput Biol Med, vol. 184, p. 109438, Jan. 2025, doi: 10.1016/J.COMPBIOMED.2024.109438.
[18] Z. Gao et al., “Identification and cognitive function prediction of Alzheimer’s disease based on multivariate pattern analysis of hippocampal volumes,” https://doi.org/10.1177/13872877241296130, Nov. 2024, doi: 10.1177/13872877241296130.
[19] S. Gowroju, S. Choudhary, A. Jain, and R. Srilakshmi, “Classification of Moderate and Advanced Dementia Patients Using Gradient Boosting Machine Technique: Classification of Moderate and Advanced Dementia Patients,” https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-6303-4.ch011, pp. 261–288, Jan. 1AD, doi: 10.4018/979-8-3693-6303-4.CH011.
[20] D. S. P, H.-J. Yang, S.-R. Kang and S.-H. Kim, “ADxClass: Multi-Domain Attention Fusion and Imputation of Missing Heterogeneous Tabular Data.”
[21] G. H. Shah, K. C. Waterfield, T. Nguyen, and S. Rajamani, “Editorial: Examining Upstream to Understand Downstream -Telehealth and Innovative Interventions for Advancing Health Equity,” Front Public Health, vol. 12, p. 1529825, doi: 10.3389/FPUBH.2024.1529825.
[22] A. Crimi, “Using Portable Consumer-Grade EEG Data as a Neurophysiological Biomarker for Cognitive Impairment Assessment in real-world scenarios through Machine Learning,” Nov. 2024, doi: 10.31219/OSF.IO/549QX.
[23] G. H. Shah, K. C. Waterfield, T. Nguyen, and S. Rajamani, “Editorial: Examining Upstream to Understand Downstream -Telehealth and Innovative Interventions for Advancing Health Equity,” Front Public Health, vol. 12, p. 1529825, doi: 10.3389/FPUBH.2024.1529825.
[24] M. Sha and M. P. Rahamathulla, “Quantum deep learning in Parkinson’s disease prediction using hybrid quantum–classical convolution neural network,” Quantum Inf Process, vol. 23, no. 12, pp. 1–32, Dec. 2024, doi: 10.1007/S11128-024-04588-3/METRICS.
[25] M. Sha and M. P. Rahamathulla, “Quantum deep learning in Parkinson’s disease prediction using hybrid quantum–classical convolution neural network,” Quantum Inf Process, vol. 23, no. 12, pp. 1–32, Dec. 2024, doi: 10.1007/S11128-024-04588-3/METRICS.
[26] Y. Wang et al., “Psychopathological symptom network structure in transgender and gender queer youth reporting parental psychological abuse: a network analysis,” BMC Med, vol. 19, no. 1, p. 215, Dec. 2021, doi: 10.1186/S12916-021-02091-5.
[27] S. Jahan et al., “Explainable AI-based Alzheimer’s prediction and management using multimodal data,” PLoS One, vol. 18, no. 11, p. e0294253, Nov. 2023, doi: 10.1371/JOURNAL.PONE.0294253.
[28] C. H. Chang, C. H. Lin, and H. Y. Lane, “Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease,” International Journal of Molecular Sciences 2021, Vol. 22, Page 2761, vol. 22, no. 5, p. 2761, Mar. 2021, doi: 10.3390/IJMS22052761.
[29] Z. Yao et al., “Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images,” Eur J Radiol, vol. 165, p. 110934, Aug. 2023, doi: 10.1016/J.EJRAD.2023.110934.
[30] M. Kale et al., “AI-driven innovations in Alzheimer’s disease: Integrating early diagnosis, personalized treatment, and prognostic modelling,” Ageing Res Rev, vol. 101, p. 102497, Nov. 2024, doi: 10.1016/J.ARR.2024.102497.
[31] K. Aditya Shastry and H. A. Sanjay, “Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review,” Multimed Tools Appl, vol. 83, no. 13, pp. 40057–40092, Apr. 2024, doi: 10.1007/S11042-023-16928-Z/METRICS.
[32] S. Khandakar, “Unveiling Early Detection And Prevention Of Cancer: Machine Learning And Deep Learning Approaches:,” Educational Administration Theory and Practice, pp. 14614–14628, May 2024, doi: 10.53555/KUEY.V30I5.7014.
[33] A. AlMohimeed, M. Shehata, N. El-Rashidy, S. Mostafa, A. Samy Talaat, and H. Saleh, “ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine,” Bioengineering 2024, Vol. 11, Page 729, vol. 11, no. 7, p. 729, Jul. 2024, doi: 10.3390/BIOENGINEERING11070729.
[34] S. Nerella et al., “Transformers in Healthcare: A Survey.”
[35] M. S. Islam et al., “Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications,” Canadian Journal of Cardiology, vol. 40, no. 10, pp. 1788–1803, Oct. 2024, doi: 10.1016/J.CJCA.2024.07.003.
[36] R. Damaševičius, S. K. Jagatheesaperumal, R. N. V. P. S. Kandala, S. Hussain, R. Alizadehsani, and J. M. Gorriz, “Deep learning for personalized health monitoring and prediction: A review,” Comput Intell, vol. 40, no. 3, Jun. 2024, doi: 10.1111/coin.12682.
[37] S. Ganesh, T. Chithambaram, N. R. Krishnan, D. R. Vincent, J. Kaliappan, and K. Srinivasan, “Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review,” Diagnostics 2023, Vol. 13, Page 3592, vol. 13, no. 23, p. 3592, Dec. 2023, doi: 10.3390/DIAGNOSTICS13233592.
[38] Z. Khan, T. Adil, M. O. Oduoye, B. S. Khan, and M. Ayyazuddin, “Assessing the knowledge, attitude and perception of Extended Reality (XR) technology in Pakistan’s Healthcare community in an era of Artificial Intelligence,” Front Med (Lausanne), vol. 11, p. 1456017, 2024, doi: 10.3389/FMED.2024.1456017/FULL.
[39] P. Lv, J. Wang, and H. Wang, “2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT,” Biomed Signal Process Control, vol. 75, May 2022, doi: 10.1016/j.bspc.2022.103567.
[40] Q. Xiang et al., “Quantum classical hybrid convolutional neural networks for breast cancer diagnosis,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–13, Oct. 2024, doi: 10.1038/s41598-024-74778-7.
