Deep neural networks could assess neurological problems
Scientists at the Canadian Centre for Behavioural Neuroscience (CCBN), based at the University of Lethbridge, have demonstrated how deep neural networks can be taught to accurately assess neurological problems in mice.
Published in the journal PLOS Biology, the research lays the foundation for a future where patients with neurological disorders, who may have impaired movement, can have their conditions assessed remotely rather than needing to meet with a doctor face to face.
Using rats that had incurred a stroke that affected the movement of their forelimbs, the scientists first asked experts to score the rats’ degree of impairment based on how they reached for food. Then they input this information into a state-of-the-art deep neural network so that it could learn to score the rats’ reaching movements with human-expert accuracy.
When the network was subsequently given video footage from a new set of rats reaching for food, it was then also able to score their impairments with similar human-like accuracy. 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 to pull a string to obtain a food reward.
Artificial neural networks are currently used to drive cars, to interpret video surveillance and to monitor and regulate traffic. This revolution in the use of artificial neural networks has encouraged behavioural neuroscientists to use such networks for scoring the complex behaviour of experimental subjects. Similarly, neurological disorders could also be assessed automatically, allowing quantification of behaviour as part of a check-up or to assess the effects of a drug treatment. This could help avoid the delay that can present a major roadblock to patient treatment.
Altogether, this research indicates that deep neural networks such as this can provide a reliable score for neurological assessment and can assist in designing behavioural metrics to diagnose and monitor neurological disorders. Interestingly, the results revealed that this network can use a wider range of information than that included by experts in a behavioural scoring system.
A further contribution of the research is that this network was able to identify features of the behaviour that are most indicative of motor impairments. This is important because this has the potential to improve monitoring the effects of rehabilitation.
The method would thus aid standardisation of the diagnosis and monitoring of neurological disorders, and in the future could be used by patients at home for monitoring of daily symptoms.
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