Artificial Intelligence tool can detect post-traumatic stress disorder by analysing voices: Study

Scientists have developed an artificial intelligence (AI) tool that can help diagnose post-traumatic stress disorder (PTSD) by analysing patient’s voices, a study has found. The research, published in the journal Depression and Anxiety, found that an artificial intelligence tool can distinguish — with 89 per cent accuracy — between the voices of those with or without PTSD.

“Our findings suggest that speech-based characteristics can be used to diagnose this disease, and with further refinement and validation, may be employed in the clinic in the near future,” said Charles R Marmar, from New York University.

More than 70 per cent of adults worldwide experience a traumatic event at some point in their lives, with up to 12 per cent of people in some struggling countries suffering from PTSD.

Those with the condition experience strong, persistent distress when reminded of a triggering event.

Researchers said that a PTSD diagnosis is most often determined by clinical interview or a self-report assessment, both inherently prone to biases.

This has led to efforts to develop objective, measurable, physical markers of PTSD progression, much like laboratory values for medical conditions, but progress has been slow.

In the current study, the research team used a statistical/machine learning technique, called random forests, that has the ability to “learn” how to classify individuals based on examples.

Such AI programmes build “decision” rules and mathematical models that enable decision-making with increasing accuracy as the amount of training data grows.

The researchers first recorded standard, hours-long diagnostic interviews, called Clinician-Administered PTSD Scale (CAPS) of 53 Iraq and Afghanistan veterans with military-service-related PTSD, as well as those of 78 veterans without the disease.

The recordings were then fed into voice software from SRI International—the institute that also invented Siri—to yield a total of 40,526 speech-based features captured in short spurts of talk, which the team’s AI programme sifted through for patterns.

The random forest programme linked patterns of specific voice features with PTSD, including less clear speech and a lifeless, metallic tone, both of which had long been reported anecdotally as helpful in diagnosis.

While the current study did not explore the disease mechanisms behind PTSD, the theory is that traumatic events change brain circuits that process emotion and muscle tone, which affects a person’s voice.

“The speech analysis technology used in the current study on PTSD detection falls into the range of capabilities included in our speech analytics platform called SenSay Analytics,” said Dimitra Vergyri, director of SRI International’s Speech Technology and Research (STAR) Laboratory.