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Improving clarity of health diagnostic tools

Dr. Andrew Hamilton-Wright

Headshot of Dr. Hamilton-Wright

“We need diagnostic tools that tell us what’s happening at the physiological level, not just at the machine level.”

Dr. Hamilton-Wright

Medical professionals are experts at reading numbers from health diagnostic tools, but what do all the figures really mean?

Diagnostic tools – such as electromyography (EMG) to measure skeletal muscle activity – are designed to recognize a certain set of patterns in the signals they’re measuring. But they don’t give information about the underlying biological state causing the signals.

Dr. Andrew Hamilton-Wright, an associate professor in the University of Guelph’s School of Computer Science, is working to fix this disconnect and enhance the information that health diagnostic tools can tell medical professionals about patients’ conditions.

“Currently our technology might say ‘this is pattern type 3, which relates to risk factor number 5,” says Hamilton-Wright. “But that’s only part of the story. We need diagnostic tools that tell us what’s happening at the physiological level, not just at the machine level.”

Hamilton-Wright is using his computer science and software expertise to develop accurate sampling techniques, which he says will reduce the disconnect between diagnostic readings and biological states. This includes determining how many data points from these tools are needed to make a conclusion and adjusting the tools to determine when and where they work best — for example, identifying optimal electrode placement for an EMG.

To date, Hamilton-Wright has applied this research in multiple contexts and collaborated with experts from various disciplines, including psychology, bioinformatics, mechanical engineering and epidemiology. He has analyzed EMG data to understand the relationship between diseases and skeletal muscles, and has run electrodermal tests (which measure sweat production on skin) to identify patterns in stress response. As well, he’s worked with postural data to improve workplace ergonomic practices.

Much of the direct application of Hamilton-Wright’s work is in the human health realm. But animal models figure prominently in this work, as well. For example, the EMG muscle project uses frog and cat models to infer human muscle physiology (muscle structure in many vertebrates is quite similar). Hamilton-Wright and his team have learned about muscular control issues in humans, such as carpal tunnel syndrome, by introducing control issues in frog models.

“It’s an effective way to understand the experience and progression of disease in both humans and animals, taking what is learned from one and applying it to the other,” he says.

This kind of translational and comparative health research underscores a basic tenet of One Health – that is, that human, animal, and environmental health are all interconnected. One Health recognizes that health is a broad and complex topic, and Hamilton-Wright demonstrates that through his work.

“Health is not a patchwork system,” says Hamilton-Wright. “If you pull a string in one area, you have to be conscious of its impacts in another area, which is why it’s important to take a step back from your specific area of study and look at the whole system.” 

Funding for Hamilton-Wright’s work has come from a variety of sources including NSERC Discovery and Engage Grants, CIHR Foundation Grants, U of G Catalyst funding, NRC-IRAP, and the New Brunswick Innovation Foundation.

            Find out more about Hamilton-Wright and his work on his faculty webpage or lab website

Article by Marilyn Sheen and Anna McMenemy

Listen to Dr. Hamilton-Wright describe how he views One Health:

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