AI Case Study

The University of Alberta and IBM can predict schizophrenia with 74% accuracy by looking at images of the brain's blood flow

Schizophrenia impacts 1.2% of the US population. It is hard to detect but its effects can debilitating. The University of Alberta partnered with IBM to predict the onset of the disease and its severity. Using deep supervised learning a model was trained to predict the diagnosis with 74% accuracy.

Industry

Healthcare

Healthcare Providers And Services

Project Overview

The University of Alberta and IBM "research team first trained its neural network on a 95-member dataset of anonymized fMRI images from the Function Biomedical Informatics Research Network which included scans of both patients with schizophrenia and a healthy control group. These images illustrated the flow of blood through various parts of the brain as the patients completed a simple audio-based exercise. From this data, the neural network cobbled together a predictive model of the likelihood that a patient suffered from schizophrenia based on the blood flow."

Reported Results

"The results of the IBM and University of Alberta research demonstrated that, even on more challenging neuroimaging data collected from multiple sites (different machines, across different groups of subjects etc.) the machine learning algorithm was able to discriminate between patients with schizophrenia and the control group with 74% accuracy using the correlations in activity across different areas of the brain."

"Additionally, the research showed that functional network connectivity could also help determine the severity of several symptoms after they have manifested in the patient, including inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation. The prediction of symptom severity could lead to a more quantitative, measurement-based characterization of schizophrenia; viewing the disease on a spectrum, as opposed to a binary label of diagnosis or non-diagnosis. This objective, data-driven approach to severity analysis could eventually help clinicians identify treatment plans that are customized to the individual."

Technology

Limited details but likely a convolutional neural network (CNN) trained on the MRI scans to identify blood location on the brand. And likely recurrent neural networks (RNN) to look at the flow over time.

Function

R And D

Core Research And Development

Background

"Schizophrenia is not a particularly common mental health disorder in America, affecting just 1.2 percent of the population (around 3.2 million people), but its effects can be debilitating.

Benefits

Data

"The research team first trained its neural network on a 95-member dataset of anonymized fMRI images from the Function Biomedical Informatics Research Network which included scans of both patients with schizophrenia and a healthy control group.

These images illustrated the flow of blood through various parts of the brain as the patients completed a simple audio-based exercise. From this data, the neural network cobbled together a predictive model of the likelihood that a patient suffered from schizophrenia based on the blood flow."