Researchers at the University of Alberta have developed a new AI-based software––Ensemble Algorithm with Multiple Parcellations for Schizophrenia Prediction, or EMPaSchiz––that will allow physicians to identify schizophrenia in fMRI scans with 87 percent accuracy. Their research was published in NJP Schizophrenia.

“Schizophrenia is characterized by constellation of symptoms that might co-occur in patients,” said lead researcher Sunil Kalmady, PhD, in a prepared statement. “Two individuals with the same diagnosis might still present different symptoms. This often leads to misdiagnosis. Machine learning, in this case, is able to drive an evidence-based approach that looks at thousands of features in a brain scan to lead to an optimal prediction.”

Presently, most machine learning algorithms identify the presence of schizophrenia based on resting-state fMRI scans—and the same is true for Kalmady’s machine learning algorithm.

The difference is that Kalmady’s AI tools were trained on patients who were diagnosed with schizophrenia but were not medicated to treat their mental illness. This will allow for physicians to treat schizophrenia at an earlier stage.

Kalmady and colleagues used resting-state FMRI data collected from a cohort of 81 patients who were never treated with any psychotropic medications, including antipsychotics. Additionally, the researchers recruited 92 healthy volunteers who were matched for age and gender and screened to rule out psychiatric diagnoses. All subjects in both cohorts underwent fMRI.