Machine learning could find an answer to Parkinson’s progression
The mystery of how Parkinson’s disease progresses could be cracked thanks to researchers at the Australian National University (ANU) and machine learning.
Deborah Apthorp of the ANU Research School of Psychology has won funding for a study that will track early symptoms with the aim of finding possible indicators of progression, using machine learning. This research has received $138,930 over 5 years from the Perpetual Impact Philanthropy Grant.
As it stands, the type of Parkinson’s a diagnosed patient has or how quickly it will progress is hard to determine. Apthorp noted in an ANU report that some individuals can be fine for quite a while while others can experience a more rapid progression.
“There are different types of Parkinson’s that can look similar at the point of onset, but they progress very differently. We are hoping the information we collect will differentiate between these different conditions,” Apthorp said. “Ultimately we’d like doctors to be able to conduct tests that can predict how the disease is likely to progress.”
Apthrop’s research will look at brain imaging, eye tracking, visual perception and postural sway altogether.
“Human posture is an inherently unstable system, so you’re constantly making little corrections,” Apthorp said in the article. “When you get Parkinson’s disease it becomes harder and harder to maintain that upright posture, and you have to think more about it. Eventually as the disease gets further along you start to fall and have trouble walking.
