New app can detect depression before symptoms show simply by looking at your face when you unlock your phone

AN APP can use AI to detect depression from facial cues before the user even knows something is wrong, a study shows.

The MoodCapture app uses a phone’s front camera to capture a person’s facial expressions and surroundings, then evaluates the images for cues associated with the condition.

GettyAn app can use AI to detect depression from facial cues, a study shows[/caption]

It was found to correctly identify the early symptoms of depression with 75 per cent accuracy.

Professor Andrew Campbell, of Dartmouth College, New Hampshire, said: “People use facial recognition software to unlock their phones hundreds of times a day.

“MoodCapture uses a similar technology pipeline of facial recognition technology with deep learning and AI hardware, so there is terrific potential to scale up this technology without any additional input or burden on the user.

“A person just unlocks their phone and MoodCapture knows their depression dynamics and can suggest they seek help.”

About one in five UK adults experienced some form of it in 2021, according to the Office for National Statistics.

And three in ten adults in England are diagnosed with depression any given week.

The study, published on the arXiv preprint database, tested the app on 177 people diagnosed with major depressive disorder.

It captured 125,000 images of participants over the course of 90 days.

Researchers said the app’s accuracy suggests the technology could be publicly available within the next five years.

Professor Campbell added: “This is the first time that natural ‘in-the-wild’ images have been used to predict depression.

“There’s been a movement for digital mental-health technology to ultimately come up with a tool that can predict mood in people diagnosed with major depression in a reliable and non-intrusive way.

“My feeling is that technology such as this could be available to the public within five years. We’ve shown that this is doable.”

There is still, however, further to go if this app is going to become reliable, as the researchers note that an accuracy of 90 per cent would be the threshold of a viable sensor.

The team hope technologies like MoodCapture could help close the significant gap between when people with depression need intervention and the access they have to mental-health resources.

Assistant professor Nicholas Jacobson said: “Many of our therapeutic interventions for depression are centred around longer stretches of time, but these folks experience ebbs and flows in their condition. Traditional assessments miss most of what depression is.

“Our goal is to capture the changes in symptoms that people with depression experience in their daily lives.

“If we can use this to predict and understand the rapid changes in depression symptoms, we can ultimately head them off and treat them. The more in the moment we can be, the less profound the impact of depression will be.

“The goal of these technologies is to provide more real-time support without adding an additional pressure on the care system.”

WHAT DOES THE APP SUGGEST?

The researchers say that MoodCapture would ideally suggest preventive measures such as going outside or checking in with a friend instead of explicitly informing a person they may be entering a state of depression.

Dr Jacobson added: “Telling someone something bad is going on with them has the potential to make things worse.

“We think that MoodCapture opens the door to assessment tools that would help detect depression in the moments before it gets worse.

“These applications should be paired with interventions that actively try to disrupt depression before it expands and evolves.

“A little over a decade ago, this type of work would have been unimaginable.”

The team note that their next steps will involve training the AI on a greater diversity of participants, improving its diagnostic ability, and reinforcing privacy measures.

They are hoping to create an iteration of MoodCapture for which photos never leave a person’s phone and are instead processed on a user’s device to extract facial expressions associated with depression and convert them into code for the AI model.

PhD candidate Subigya Nepal said: “Even if the data ever does leave the device, there would be no way to put it back together into an image that identifies the user.

“You wouldn’t need to start from scratch-we know the general model is 75 per cent accurate, so a specific person’s data could be used to fine-tune the model. Devices within the next few years should easily be able to handle this.

“We know that facial expressions are indicative of emotional state. Our study is a proof of concept that when it comes to using technology to evaluate mental health, they’re one of the most important signals we can get.”