neurosciencenews:

Artificial intelligence technology is able to objectively differentiate between those with PTSD and those without by analyzing speech samples, with 89.1% accuracy.

Read the full article here: https://neurosciencenews.com/ai-ptsd-voice-12025/

Source: NYU Langone Health

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

(The study authors say 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. The image is adapted from the NYU Langone Health news release. )

The researchers first recorded standard, hours-long diagnostic interviews, called Clinician-Administered PTSD Scale, or 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 program sifted through for patterns.

The random forest program 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.

Closed access
“Speech‐based markers for posttraumatic stress disorder in US veterans”
Charles R. Marmar, Adam D. Brown, Meng Qian, Eugene Laska, Carole Siegel, Meng Li, Duna Abu‐Amara, Andreas Tsiartas, Colleen Richey, Jennifer Smith, Bruce Knoth, Dimitra Vergyri. Depression and Anxiety 22 APR 2019 doi:10.1002/da.22890

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