Is AI the Future of Alzheimer's Diagnostics?

Artificial intelligence (AI) tools are becoming part of healthcare decisions. AI-based algorithms can help diagnose and treat many medical conditions. They work better than doctors at many tasks. The US Food and Drug Administration (FDA) has approved hundreds of medical AI devices.^1,2

Diagnosing Alzheimer's at early stages is key. At the stage of mild cognitive (thinking) impairment, called MCI, the signs are hard to see. AI-powered tools can detect these signs to diagnose Alzheimer's earlier. This can get people the best possible treatment options earlier.³

Transforming Alzheimer's diagnostics with AI

Doctors currently use many tests to diagnose Alzheimer's. A doctor will evaluate:³

1. Symptoms
2. Medical history
3. Memory, thinking, and language scores from tools used to measure cognition

Other tests can observe brain structures for signs of Alzheimer's. Such tests include:³

• Magnetic resonance imaging (MRI)
• Positron emission tomography (PET)
• Looking for biomarkers such as amyloid and tau proteins
• Analyzing cerebrospinal fluid (CSF) samples

But these methods rely on each doctor's ability to analyze results. Early Alzheimer's disease has signs that are hard to detect. Brain imaging tests may not be sensitive enough to show these signs.³

AI offers a way to improve these diagnostic tests. One branch of AI is called machine learning (ML). In ML, researchers train a model on large amounts of data. A machine uses the model with new data to perform a task.^1,3

For example, ML models can make decisions about healthcare. This includes diagnosing chronic conditions based on test results. ML models make Alzheimer's diagnosis more objective and sensitive. Researchers have developed ML algorithms to:^3,4

1. Model how a disease advances to predict progression of Alzheimer's
2. Analyze MRI brain images for early signs of Alzheimer's
3. Predict Alzheimer's risk from electronic health records

Using AI with brain wave recordings

A study published in 2024 has shown that AI can boost the power of electroencephalogram (EEG) tests. EEG tests use electrodes on the scalp to monitor brain activity. EEG tests have many benefits over other tests for Alzheimer's. For example, EEG tests:^5,6

• Show different brain waves in people with cognitive problems
• Are more widely available than MRIs and PET scans
• Are less expensive and less invasive than other tests
• Can detect changes earlier than imaging tests

But analyzing data from EEG tests is hard. EEG patterns are complex and difficult to measure. EEG data often contains "artifacts." These are common errors that should be ignored. Accurate assessment of EEG patterns depends on doctors' own expertise. This often leads to biased analysis.^5,6

ML models can help address these problems. They can detect patterns that human experts cannot see. They can also use more precise numbers to describe patterns. This can enable EEGs to accurately diagnose Alzheimer's.^5,6

In the study, researchers used data from over 11,000 people who received EEGs. They used ML to simplify complex patterns into 6 specific features. This allowed them to find patterns that indicate cognitive problems.^5,6

The model could then use EEG data to identify people with cognitive problems. It could also tell whether the cause of dementia was Alzheimer's or Lewy Body dementia. EEG features matched up with results from other tests, such as PET scans and CSF analysis.^5,6

Diagnosing Alzheimer's with AI

Experts see the biggest benefit of AI in early stages of Alzheimer's. This is because it can detect and measure mild signs. Catching memory problems early is important. Newer treatments offer a chance to slow disease progression. Early diagnosis can give more people these treatment options.⁵

EEGs will not replace imaging and other tests. But AI-powered EEGs can provide a cheaper and more accessible tool. This is especially important for rural communities without easy access to special equipment.⁵

The future and risks of AI in Alzheimer's

Researchers will continue to test and validate their AI tools. This may take several years. AI models get more accurate as they get trained on more data. AI technologies are also improving quickly. In the future, there may be better ways to integrate AI into diagnosis.⁵

Regulatory questions also need to be answered. In the United States, the FDA approves medical devices. The FDA releases summary documents that describe the device and its performance. But the FDA's typical ways to approve and regulate a device were not designed for AI devices.^1,2,7

The FDA is adapting to ensure that AI devices are properly evaluated. Major risks of AI devices include biased training data and differences in human-AI interactions. Addressing these risks is important to ensure AI tools are useful in diagnosing Alzheimer's.^1,2,7