Using this novel form of artificial intelligence developed by UC and Northwestern, the technology was able to predict whether a respondent was likely to suffer from a higher or lower level of anxiety with up to 81% accuracy. The system also scored high in sensitivity and specificity – performance measures indicating how well the model classified people who have higher anxiety and those who don’t.
Bari noted “the picture-rating task can be used to produce daily and unbiased snap shots of a person’s mental health status without asking direct questions which may trigger negative or upsetting feelings.”
She added that direct questions about anxiety may be less effective over time as respondents eventually tend to answer the same questions by rote. The new technology is also independent of native language and can be widely used across a variety of settings to assess anxiety.”
Researchers noted that the anonymous data were sampled from the U.S. population during the COVID-19 pandemic, during which there were reports of higher-than-normal rates of loneliness and anxiety.
The study was funded by two grants to Breiter from the U.S. Office of Naval Research. Breiter, Bari and Nicole Vike, along with engineer Byoungwoo Kim, collaborated on the study with investigators from Northwestern University including Shamal Lalvani, Leandros Stefanopoulos, Martin Block, and Katsaggelos (co-senior author) and Nicos Maglaveras from The Aristotle University of Thessalonik.
Massachusetts General Hospital (Harvard School of Medicine) was also actively associated with this work, where the research team led by Breiter initially uncovered the computational cognition underlying the work.
Featured image at top: Senior Research Associate Sumra Bari is investigating new health applications for artificial intelligence in UC’s College of Engineering and Applied Science. Photo/Andrew Higley/UC Marketing + Brand