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Smartphone app equipped with AI detects onset of depression from facial expressions

Smartphone app equipped with AI detects onset of depression from facial expressions

 


Subigya Nepal, PhD Candidate

Credit: LinkedIn

Dartmouth investigators have developed the first smartphone application that uses artificial intelligence (AI) to detect the onset of depression based solely on facial expressions. 1

If this can be used to predict and understand rapid changes in depression symptoms, it could ultimately be suppressed and treated, says biomedical data from Dartmouth's Center for Technology and Behavioral Health. researcher Nicholas Jacobson, assistant professor of science and psychiatry, said in the paper. Press Release.2 The more you focus on the present moment, the less severe the effects of depression will be.

Researcher Andrew Campbell, Dartmouth's Albert Bradley 1915 3rd Century Professor of Computer Science, said the promising results suggest smartphone apps could be available to the public within five years. When that happens, people will pull out their smartphones, and just as facial recognition unlocks their phones, the same feature could detect the onset of depression.

The app MoodCapture accurately diagnosed 75% of participants in a clinical trial as having early symptoms of depression.

Campbell said in a press release that this is the first time images of the natural world have been used to predict depression. Ultimately, there is a movement in digital mental health technology to develop tools that can reliably, non-intrusively predict the mood of people diagnosed with major depression. ”

A study published this year in the American Journal of Psychiatry found that brain scans programmed with an AI algorithm predicted whether the antidepressant sertraline would work within a week, instead of waiting six to eight weeks. I found out that it can be done. AI has been developed in the field of major depressive disorder from a detection and treatment perspective.

The study, led by Dartmouth doctoral candidates Subigya Nepal and Arvind Pillai, involved 177 people with a pre-existing diagnosis of major depressive disorder, recruited through online ads on Google and Facebook. It was included. 1 Participants had bipolar disorder, active suicidality, or psychosis. Most participants were female (86.4%) and Caucasian (83.6%).

Before the study, participants had agreed to have their photo taken with their phone's front camera, but they did not know when this would occur. The app took 125,000 photos of participants' girlfriends over 90 days at random points in time.

MoodCapture uses AI-powered facial image processing software to detect the onset of AI before the user notices anything unusual. The app uses your phone's front camera to capture candid natural shots of a person's facial cues and surroundings, and captures images when someone unlocks their phone.

MoodCapture uses a similar technology pipeline of facial recognition technology with deep learning and AI hardware, so there is great potential to scale up this technology without any additional input or burden on users. says Campbell. 2 “A person just unlocks his phone and his MoodCapture.” Knowing the dynamics of their depression, you can suggest they seek help.

Using the first group of participants, researchers programmed MoodCapture to recognize depression through facial cues. As they answered questions, the front-facing camera would randomly take a series of pictures and I would feel depressed, depressed, and helpless. This question was extracted from the 8-item Patient Health Questionnaire (PHQ-8).

The team then used image analysis AI on the photos, allowing the MoodCaptures predictive model to learn to associate depression with specific facial expressions, such as gaze, eye movements, head position, and muscle stiffness. The background was also analyzed in terms of color, lighting, location of the photo, and number of people in the photo.

AI programs collect patterns in past images, so if someone looks flat in a dimly lit room for an extended period of time, predictive models may infer that the person is experiencing an onset of depression. there is.

A second group of participants tested the model, answered the same PHQ-8 questions, and based on the first group's data captured and analyzed participants' photos in the app to identify facial cues related to depression. I had it analyzed. The model correctly determined that three-quarters of the participants had depression.

Despite the study's positive results, the researchers noted that the sample size was small, with a large proportion of white women, that they used self-reported depression scores, and that clinically depressed They highlighted several limitations, including including only patients, not including healthy controls, and not investigating other factors associated with depression. social interactions, physical activity, and the environment. They also highlighted the need to ensure user consent, data security, and transparency in the use of personal data as MoodCapture may have ethical and privacy concerns. .

Jacobson believes that MoodCapture opens the door to assessment tools that can help detect depression in the moments before it worsens.2 It needs to be combined with interventions that actively try to disrupt depression before it can evolve. A little over 10 years ago, this type of work would have been unimaginable.

References

Nepal, S., Pillai, A., Collins, A., et al. MoodCapture: Depression detection using real smartphone images. 2024. https://doi.org/10.1145/3613904.3642680. A smartphone app uses AI to detect depression from facial cues. Yurek Alert! February 27, 2024. https://www.eurekalert.org/news-releases/1035029. Accessed February 27, 2024. Derman, C. AI brain scans can predict whether antidepressant sertraline will work. HCP live. February 15, 2024. https://www.hcplive.com/view/brain-scan-ai-can-predict-whether-the-antidepressant-sertraline-will-work. Accessed February 27, 2025.

Sources

1/ https://Google.com/

2/ https://www.hcplive.com/view/smartphone-app-ai-detects-depression-onset-facial-expressions

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