A research team from the University of South Florida (USF) has developed an AI system that can identify post-traumatic stress disorder (PTSD) in children.
The project addresses a longstanding clinical dilemma: diagnosing PTSD in children who may not have the emotional vocabulary, cognitive development or comfort to articulate their distress. Traditional methods such as subjective interviews and self-reported questionnaires often fall short. This is where AI steps in.
“Even when they weren’t saying much, you could see what they were going through on their faces,” Alison Salloum, professor at the USF School of Social Work, reportedly said. Her observations during trauma interviews laid the foundation for collaboration with Shaun Canavan, an expert in facial analysis at USF’s Bellini College of Artificial Intelligence, Cybersecurity, and Computing.
The study introduces a privacy-first, context-aware classification model that analyses subtle facial muscle movements. However, instead of using raw footage, the system extracts non-identifiable metrics such as eye gaze, mouth curvature, and head position, ensuring ethical boundaries are respected when working with vulnerable populations.
“We don’t use raw video. We completely get rid of subject identification and only keep data about facial movement,” Canavan reportedly emphasised. The AI also accounts for conversational context, whether a child is speaking to a parent or a therapist, which significantly influences emotional expressivity.
Across 18 therapy sessions, with over 100 minutes of footage per child and approximately 185,000 frames each, the AI identified consistent facial expression patterns in children diagnosed with PTSD. Notably, children were more expressive with clinicians than with parents; a finding that aligns with psychological literature suggesting shame or emotional avoidance often inhibits open communication at home.
While still in its early stages, the tool is not being pitched as a replacement for therapists. Instead, it’s designed as a clinical augmentation, a second set of ‘digital’ eyes that can pick up on emotional signals even trained professionals might miss in real time.
“Data like this is incredibly rare for AI systems,” Canavan added. “That’s what makes this so promising. We now have an ethically sound, objective way to support mental health assessments.”
If validated on a larger scale, the system could transform mental health diagnostics for children—especially for pre-verbal or very young patients—by turning non-verbal cues into actionable insights.
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