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Did you know that a simple brainwave test, known as electroencephalography (EEG), might hold the key to predicting Alzheimer’s disease? With over 55 million people globally affected by dementia—a number expected to triple by 2050—the need for early, accessible, and accurate diagnostic tools has never been more urgent. Traditional diagnostic methods, though effective, are expensive, invasive, and often inaccessible. Now, the pairing of EEG with artificial intelligence (AI) offers a transformative approach to early detection, providing hope for millions. 

Diagnosing dementia in its early stages is critical. It allows patients and their families to make informed decisions, access treatments that could slow progression, and improve quality of life. Yet, existing diagnostic tools have significant limitations. Tests such as PET scans and cerebrospinal fluid (CSF) analysis are expensive and require specialized facilities and expertise, which limits their availability. Additionally, these procedures are often inaccessible in resource-limited regions. Even in areas with advanced medical care, memory tests—the most common initial diagnostic tool—are prone to inaccuracies, resulting in misdiagnosis or delayed recognition of cognitive decline. This reality shows the need for innovative, affordable alternatives that work across diverse healthcare systems. EEG offers a compelling solution to this challenge. 

EEG, which measures brainwave activity via small electrodes placed on the scalp, is painless, widely accessible, and significantly less expensive than imaging techniques like PET or MRI. Its unique strength lies in capturing real-time brain function. Research has demonstrated that specific brainwave patterns, such as slowed alpha waves or heightened delta waves, can indicate early-stage Alzheimer’s disease even before visible structural damage occurs. These functional insights make EEG a powerful tool for early detection. For instance, one study by Cassani et al analyzed EEG data from thousands of individuals and successfully identified patterns distinguishing Alzheimer’s from other types of dementia (e.g., Lewy body dementia). By uncovering these early warning signs, EEG holds the potential to revolutionize how we diagnose and treat dementia. 

While EEG alone is transformative, combining it with AI takes its potential to the next level. Artificial intelligence excels at identifying subtle patterns in complex data far beyond human capability. Machine learning models can process vast amounts of EEG data to detect changes associated with mild cognitive impairment (MCI) and predict its progression to Alzheimer’s disease. For example, a study by Lee et al demonstrated that AI-guided analysis achieved over 81% accuracy in predicting whether MCI would advance to Alzheimer’s. These insights allow clinicians to intervene earlier with tailored strategies to slow cognitive decline. Moreover, advancements in AI ensure that the results are clear and understandable, enabling healthcare providers to trust and utilize these tools in real-world settings. 

The combination of EEG and AI promises to transform dementia care in profound ways. Early detection provides patients with critical time to prepare, pursue emerging treatments, and implement lifestyle changes that could slow disease progression. For families, this timely diagnosis reduces uncertainty, empowering them to plan for care and support. From a healthcare perspective, EEG is an affordable and non-invasive diagnostic tool, making it especially valuable in areas with limited resources. Adding AI to EEG analysis also helps researchers find detailed brain activity patterns, leading to more insights and effective treatments. 

Despite its promise, the widespread adoption of AI-enhanced EEG diagnostics faces challenges. Data variability across different populations and healthcare settings requires further validation to ensure that AI models perform accurately in diverse environments. Additionally, standardizing clinical protocols for EEG collection and AI analysis is essential for ensuring consistency and reliability. Future advancements may address these hurdles by combining EEG data with other non-invasive diagnostic tools, such as blood-based biomarkers, to improve diagnostic precision. With ongoing research and investment, these technologies will become more accessible and impactful, offering hope to millions affected by dementia. 

The fusion of EEG and AI is a game-changer for dementia care. By enabling early, accurate, and affordable diagnosis, these tools promise better outcomes for patients, families, and healthcare systems worldwide. As dementia cases continue to rise, adopting such groundbreaking

innovations is not just an opportunity—it is a necessity. These advancements provide the means to improve lives, reduce inequities in healthcare access, and alleviate the immense burden of this global health crisis. Advocating for greater investment and integration of these tools into routine clinical practice is essential for transforming how we understand, diagnose, and ultimately treat one of the most pressing healthcare challenges of our time.

Note: Eukaryon is published by students at Lake Forest College, who are solely responsible for its content. The views expressed in Eukaryon do not necessarily reflect those of the College. Articles published within Eukaryon should not be cited in bibliographies. Material contained herein should be treated as personal communication and should be cited as such only with the consent of the author.

References 

Cassani et al. (2021): Cassani, R., Estarellas, M., San-Martin, R., Fraga, F. J., & Falk, T. H. (2021). Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases. Brain Communications, 3(1).

Lee et al. (2024): Lee, H. H., Chen, X., Lee, S. J., & Tan, R. (2024). Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings. ClinicalMedicine, Volume 74, 2024.