October 20th, 2020

U of L neurology researchers developing app

By Bobinec, Greg on November 29, 2019.

Greg Bobinec

Lethbridge Herald


Hardeep Ryait, Ian Whishaw and Artur Luczak of University of Lethbridge Canadian Centre for Behavioral Neuroscience and their colleagues are proposing a smartphone app to interpret neurological disorder movements on video and sends the results to their doctor.

In their paper, Data-driven analyses of motor impairments in animal models of neurological disorders, published in the journal PLOS Biology, propose the app could be used with any disorder that affects movement and would significantly reduce costs of doing clinical trials for new drugs for disorders like Parkinson’s disease.

To start the project, they asked people with special training to score the quality of reaches for food made by rats that had suffered a stroke that impaired their movements. They then provided the information to a state-of-the-art deep neural network, a type of machine learning that simulates the brain’s neural network, so that it could learn to score the rats’ reaching movements with human-expert accuracy.

“When the network was subsequently given video from a new group of rats reaching for food, it scored their impairments with human accuracy,” says Ryait, post-doctorial fellow in Luczak’s lab at the time, in a release from the U of L.

The same program proved able to score other tests given to rats and mice, including tests of their ability to walk across a narrow beam and pull a string to obtain a food reward.

“Intelligent neural networks are being implemented to drive cars, interpret video surveillance and monitor and regulate traffic,” says Luczak. “This revolution in the use of neural networks has encouraged behavioural neuroscientists to use networks to evaluate movement disorders.”

The study shows the potential for neurological disorders to also be assessed automatically and behaviour quantified as part of a checkup or to assess the effects of a drug treatment.

“The delay in the assessment of neurological diseases is often a major roadblock in patient treatment,” says Whishaw. “This research indicates the network can provide a reliable score for neurological assessment and this breakthrough can assist in designing behavioural indexes to diagnose and monitor neurological disorders.”

The results also revealed the network can use a wider range of information than that included by trained humans in a behavioural scoring system. The network was able to identify features of a behaviour that are most indicative of motor impairment which can, in turn, improve monitoring of rehabilitation. This method would help standardize diagnosis and monitoring of neurological disorders and could be used by patients at home to monitor daily symptoms.

The application could be used with any disorder that affects movement and would significantly reduce the costs of doing clinical trials for new drugs for disorders like Parkinson’s disease. Beyond the health field, the application could also be used in sports to help players perfect their golf swing or score a basket.

“Now, we are looking for funding to create a smartphone app,” says Luczak. “Here we have showed that it works in principle in a big computer cluster. It requires more work to adapt it to a smartphone, but it is completely doable and we are interested in pursuing it.”

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