How Instagram shots might beat doctors in spotting depression
How Instagram shots might beat doctors in spotting depression
Image Shutterstock KatePh
09 August, 2017, 00:28
This method of rating the photos was far less reliable than a general practitioner's success rate of unassisted diagnosis, according to the study. The control participants, who had no record of depression, used the bright filter "Valencia" most frequently. (Roughly half of participants had reportedly suffered from clinical depression within the past three years).
As it turned out, depressed people were actually more likely to post photos with faces in them, but when healthy people did post images with faces, there tended to be more people in the photographs.
That matches the research that has linked depression to reduced social interaction.
Researchers from the University of Vermont and Harvard developed a computer algorithm that was able to identify individuals with signs of depression by the types of images they share on social media.
When the researchers analyzed the almost 44,000 images, they found that posts from users who had a diagnosis of depression were likely to be bluer, grayer and darker than posts from users without the condition. That could support - although the authors emphasize this is untested - a "sad-selfie hypothesis", in that depressed people may post more photos of only themselves. Yet, those volunteers were not able to complete the task as efficiently as the statistical computer model; the rating done by humans lacked much correlation with the features of the photos that were identified by the computer. Using a photo filter was less common among the individuals with depression diagnoses than among those without it. The volunteers provided the researchers with information about past diagnoses of depression and responded to a questionnaire created to assess a person's level of depression. Pixel analysis of the photos in our dataset revealed that depressed individuals in our sample tended to post photos that were, on average, bluer, darker, and grayer than those posted by healthy individuals.
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In their paper published in the journal EPJ Data Science, Danforth and Reece report the algorithm, using clinically verified insights from psychological practice, correctly identified the participants with a history of depression in 70% of cases.
"It's a proof of concept and for the particular individuals we studied, this set of predictors works for them".
"We were looking for subtle patterns associated with depression, and that required sifting through a lot of data to be confident about what we were seeing", Reece said.
He clarifies that it's "not yet a diagnostic test, not by a long shot".
According to Danforth, there is a need for more thinking on the morality of machines.
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