
==== Front
J Clin Neurol
J Clin Neurol
JCN
Journal of Clinical Neurology (Seoul, Korea)
1738-6586
2005-5013
Korean Neurological Association

39227341
10.3988/jcn.2024.0288
Letter to the Editor
Potential Benefits of Using Artificial Intelligence to Diagnose Alzheimer’s Disease
https://orcid.org/0009-0005-3096-7153
Cecot Jakub a
https://orcid.org/0009-0008-2632-4333
Zarzecki Konrad b
https://orcid.org/0009-0009-8621-7756
Mandryk Miłosz b
a Internal Medicine Institute, University Clinical Hospital, Wrocław, Poland.
b Clinical Department of Internal Medicine, 4th Military Clinical Hospital, Wrocław, Poland.
Correspondence: Jakub Cecot, MD. Internal Medicine Institute, University Clinical Hospital, Borowska 213, Wrocław 50-556, Poland. Tel +48-575251098, jakubcecot1@gmail.com
9 2024
12 8 2024
20 5 548549
25 6 2024
05 7 2024
07 7 2024
Copyright © 2024 Korean Neurological Association
2024
Korean Neurological Association
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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pmcDear Editor,

This letter highlights some recent trends in the use of artificial intelligence (AI) to diagnose and manage Alzheimer’s disease (AD). As reported in the editorial by Kwon and Koh1 population aging is resulting in AD becoming more common worldwide. The estimates for the future prevalence of AD are alarming, and point to an impending crisis. As demonstrated in the aforementioned article, monitoring of population and risk groups is an important aspect of managing AD and dementia. Unfortunately, this can be problematic due to large workload and resource requirements. The use of AI algorithms has the potential to lighten this burden, translating into shorter diagnosis times, which is especially important as more disease-modifying drugs being available.

AI has proven to be useful in numerous medical fields. Its ability to process vast amounts of data far exceeds human capabilities, allowing for much more efficient interpretations of medical data, which is key to improving the diagnosis process. The most-apparent and best-studied aspect of applying AI to AD diagnostics is neuroimaging interpretation. For example, the review article by Bazarbekov et al.2 reports on various studies utilizing a combination of deep-learning and machine-learning algorithms with various neuroimaging techniques, including magnetic resonance imaging, positron-emission tomography, and electroencephalography. That review demonstrates that despite its limitations, AI has the potential to revolutionize AD management by allowing earlier diagnoses. Nowadays, AI models can reach outstandingly high levels of efficiency and accuracy. For example, Khalid et al.3 presented an algorithm exhibiting an accuracy of 99.7% and sensitivity of 99.64% in distinguishing between various disease stages. This high performance was achieved by using a combination of different deep transfer learning models.

Another area where AI has great potential is in interpreting functional magnetic resonance imaging (fMRI) data on the relationship between cerebral flow and the activity of specific brain areas. fMRI can improve the understanding of AD by revealing functional connectivity, which are the patterns of interactions of brain areas over time. Unlike traditional structural imaging techniques, functional connectivity measures are thought to be useful in detecting early-stage AD before significant neurodegeneration and disease progression have occurred.4 Research has shown that fMRI can aid diagnoses of AD with high accuracy, but it has numerous limitations. For example, fMRI data contain large amounts of noise and are incredibly difficult to process and analyze due to the huge computational requirements. It has recently been proposed that the diagnostic limitations of fMRI can be overcome by adopting deep-learning models in clinical practice. Studies have produced promising outcomes, with the diagnostic accuracy for AD ranging between approximately 70% and 98% depending on the algorithm used.5

The interpretation of neuroimaging data is not the only application of AI in AD management. Another potential approach is identifying biomarkers, both for diagnoses and for predicting the risk of developing the disease in the future. Researchers have identified inexpensive biomarkers such as C-reactive protein and apolipoprotein plasma concentrations, which, with the help of gradient booster approaches, can detect AD with an efficacy similar to more-expensive and invasive tests.6 This has the potential to help establish low-cost screening programs for general population and risk groups.

Early AD diagnosis provides the opportunity for timely and customized interventions, and improved patient care, which may improve the quality of life. Adopting AI in clinical practice can boost the capabilities of the healthcare system; however, this is a novel application of the technology, and it has several limitations. Ethical aspects are playing a major role in how AI solutions are adapted to clinical practice, such as to address concerns7 regarding privacy when processing patient medical records. We also share the unease about potential errors caused by inadequate training data, which is one of the most commonly encountered weak points of many AI research articles. An AI algorithm might process the data of actual patients completely differently from how it performed during the training process, indicating the need for human oversight of obtained results. Further research is therefore needed into transparent protocols and guidelines that can reap of the benefits of AI in medical practice.

Availability of Data and Material

Data sharing not applicable to this article as no datasets were generated or analyzed during the study.

Author Contributions: Conceptualization: Jakub Cecot.

Data curation: all authors.

Investigation: all authors.

Visualization: Miłosz Mandryk.

Writing—original draft: Jakub Cecot.

Writing—review & editing: Miłosz Mandryk, Konrad Zarzecki.

Conflicts of Interest: The authors have no potential conflicts of interest to disclose.

Funding Statement: None
==== Refs
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2 Bazarbekov I Razaque A Ipalakova M Yoo J Assipova Z Almisreb A A review of artificial intelligence methods for Alzheimer’s disease diagnosis: insights from neuroimaging to sensor data analysis Biomed Signal Process Control 2024 92 106023
3 Khalid A Senan EM Al-Wagih K Al-Azzam MMA Alkhraisha ZM Automatic analysis of MRI images for early prediction of Alzheimer’s disease stages based on hybrid features of CNN and handcrafted features Diagnostics (Basel) 2023 13 1654 37175045
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