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The role of AI in early detection of dementia

By Mike Battista, Director, Science and Research, Creyos

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Dementia is a debilitating condition affecting millions all over the world, characterised by a decline in cognitive functions such as memory, reasoning, and communication.

Early detection is advantageous in planning for and managing diseases that cause dementia, as early detection can identify mild issues with cognition while pathology in the brain is still minimal, and potential interventions have the greatest chance of slowing decline.

Early modification of lifestyle and medical risk have been estimated to prevent 40 per cent of dementia cases.

Artificial Intelligence (AI) is emerging as a ubiquitous tool in healthcare, offering new hope for early dementia detection through advanced data analysis and pattern recognition.

Dementia consists of a range of symptoms that can include memory loss, confusion, atypical behaviour, and difficulty completing everyday activities.

Mike Battista

Diagnosing dementia early is challenging with traditional methods, as so many of the most common diagnostic tools rely on significant cognitive decline.

However, early detection can lead to better management of dementia slowing the progression of symptoms and improving the overall quality of life for patients.

With these benefits in mind, innovative uses of AI are needed to create more sensitive tools that can detect the most subtle signs of dementia earlier than ever before.

AI in Identifying Dementia Markers

In relation to dementia, AI can search extensive datasets to find markers indicative of the condition.

Then, by analysing massive amounts of data, AI is advancing healthcare capabilities by identifying patterns indicative of the condition that clinicians might miss.

Understanding these data markers is imperative for accurate detection.

For instance, AI can analyse cognitive performance, language use, and even facial expressions to differentiate between healthy individuals and those showing early signs of dementia.

A strong understanding of these markers comes from reviewing scientific literature and adopting a data-driven approach.

For researchers, AI systems can be trained on large datasets to help discover the subtle differences in performance and behaviour associated with early dementia.

Clinicians can make use of the models trained by researchers to accurately identify patients with early signs of dementia.

Data from clinicians can also be used to further train AI models, improving the accuracy of early detection over time.

The Importance of High-Quality Data

High-quality, clean data are essential for AI to function effectively in detecting dementia.

These data must be accurate and comprehensive, containing the “truth” about individuals’ health status.

One of the biggest challenges in healthcare AI is obtaining this level of data, as it involves building a repository of information that includes both individuals with dementia and healthy individuals.

To overcome this challenge, there are various initiatives to gather diverse data.

These include gathering diagnostic information from patients and creating data partnerships between healthcare practices and research organisations.

These data partnerships are not only interesting for research purposes but also help gather high-quality real-world data from patient populations that can be used to train AI systems for practical clinical application.

Longitudinal datasets are also key to achieving early diagnosis.

They are required to answer research questions about which early signs progress into dementia, which lifestyle factors slow progression over time, and more.

However, gathering these datasets is a complex process.

It requires collecting data from a large group of healthy individuals at a specific time and tracking who develops conditions like mild cognitive impairment or dementia over time.

This involves at least two time points with good follow-up, minimal dropout, and accurate diagnoses, making it a complicated dataset to get right.

Additionally, there is a great deal of selection bias in the patients who are repeatedly tested.

That is because these patients are likely more proactive about addressing their health, and/or more likely to already have issues and seek medical attention because of concerns about their cognition.

What is needed are large scale, randomised controlled studies where people are recruited ahead of time and followed throughout their health journey with state-of-the-art tools to assist in the process.

For example, the Maintain Your Brain trial followed thousands of older participants over three years to examine the effectiveness of lifestyle interventions meant to slow cognitive decline.

They used online cognitive testing from Creyos to ensure that testing was easy for participants, but provided high-quality data for the researchers.

normative dataset from healthy individuals allows researchers to compare individuals with a known condition to the general population, ensuring AI models can accurately distinguish the two groups.

Building and maintaining these datasets is a complex process but is critical for the success of AI applications.

An example of successful implementation of machine learning is the Creyos dementia screener, which used data from thousands of neurology clinic patients and a health normative database to train a model that accurately detects individuals with subtle cognitive impairments.

Current Success and Future Potential

Several success stories illustrate AI’s potential in early dementia detection.

For instance, pilot programmes using AI have shown promising results in identifying early cognitive decline through routine health assessments and patient interactions.

Examples of how AI recorded observations include:

  • How the patient moved the computer mouse while performing assessments
  • The time taken by the patient to read the tutorial
  • Passive data gathering such as device motion data (noting a person’s gait) handwriting, virtual reality integration (to test for driving ability), facial expression and speech pattern analysis.

These pilot programmes demonstrate novel ways to exploit AI’s capability to provide real-time monitoring and personalised treatment plans, tailored to individual patients’ needs.

It’s clear that the future potential of AI in dementia care is immense.

As technology advances, AI could enable even more precise and individualised healthcare, offering the earliest interventions and continuous monitoring that could further slow the progression of dementia.

This proactive approach could revolutionise how we manage and treat dementia, improving patient outcomes.

Ethical Considerations and Challenges

While AI offers great potential, it also raises ethical considerations and challenges.

Privacy concerns regarding patient data must be addressed, ensuring that data is securely handled, and patients’ confidentiality is maintained.

Additionally, the bias that can get baked into some AI systems must be scrutinised to maintain accuracy, and avoid false positives and negatives, which could lead to misdiagnosis and inappropriate treatments.

Ethical use of AI in healthcare involves balancing technological advancements with patient rights.

Furthermore, AI cannot replace human decision-making. Machine learning models and AI software can enhance human judgment, but a physicians must always make the final call when it comes to diagnosing dementia

AI holds tremendous promise in the early detection of dementia, offering tools that can analyse complex data and identify subtle signs of disruptions to cognition.

To fully realise this potential, continued research and investment in AI technologies and high-quality data collection are essential.

About the Author – Mike Battista, Director, Science and Research, Creyos 

Mike Battista is the Director of Science and Research at Creyos.

His interests and science communication focus on brain health, cognition, and neuropsychological testing.

He received his PhD in personality and measurement psychology at Western University in 2010 and has been exploring the intersection of science and technology ever since.

For more information about Creyos Health visit www.creyos.com 

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