Across campus, two of Pilarski's AI research colleagues are using the intense data-crunching capabilities that are part of the field to predict major mental illness.
Russ Greiner and Mina Gheiratmand wanted to see whether there was a different way to diagnose a person for schizophrenia. Psychiatrists with years of clinical experience and medical education are trained to make these diagnoses based on behavioural symptoms, but Greiner and Gheiratmand wanted to see whether brain scans could provide clues about a person's mental health—and the possible treatments they might need.
In collaboration with IBM, the researchers built an algorithm that learned a model for analyzing functional magnetic resonance imaging (fMRI) scans of the brain to determine whether an individual has schizophrenia. The "supervised learning" program is given a labelled dataset of scans from earlier subjects, each of whom has been identified either as having schizophrenia or not. It finds patterns in this information, which distinguishes brains that are affected by schizophrenia from those that are not.
Gheiratmand described the data elicited from the fMRI scans as "high-dimensional."
The scans produce values—such as the blood oxygen level, for example—at about 27,000 locations in the brain, explained Greiner. And each value is taken at 137 time points during the scan, resulting in about 3.7 million bits of data.
"We had 95 individuals. If you only had to look at five characteristics, you might be able to see what's going on. But instead of five features, imagine it's 5,000 features—already you can't visualize or see it. But instead of 5,000 features, it's 3.7 million," said Greiner.
"However, a computer may be able to find some particular set of patterns within the 3.7 million values that is different in patients with schizophrenia versus healthy controls; that's how patterns can be used to diagnose schizophrenia."
Greiner and Gheiratmand both said they don't think the algorithm will replace diagnosis by psychiatrists. But they both think the research could assist in psychiatrists' work. Their current learned model appeared to be fairly accurate, diagnosing cases of schizophrenia with 74 per cent accuracy.
The next step is to use the same number-crunching techniques over perhaps slightly different sets of training data to determine what treatments might work best on individual patients—or whether people will become patients at all.
"You can study youth at risk who come to a clinic, using the data from the brain scans and with models that you have trained, to predict whether an individual is probably going to develop schizophrenia in, for example, a couple of years," said Greiner.
Though the 95 subjects in the study are not considered a terribly small sample size, Greiner wonders what kind of patterns or information could be gleaned from a sample of thousands or tens of thousands of patients.
That poses a fundamental challenge for researchers such as Greiner. AI works better to elicit patterns and nuance from bigger datasets. But the data that researchers need are protected by the privacy screens and regulations that define much of the Canadian health-care system.
Regardless, Greiner sees a paradigm shift taking place that might one day make his work easier.
"There's a new generation that has cellphones, they have apps, they're storing things about their bodies on their phones," he said. "The data isn't on a sheet of paper, it's not at a doctor's office … and many patients are aware that no one is trying to hurt them. Many patients want to consent."
In Alberta right now, the Tomorrow Project is trying to get 50,000 people to open up their medical files for research such as Greiner's.
If that seems like a stretch, Greiner points out that approximately 500,000 patients in Britain have already done just that.