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The study, published in the journal Depression and Anxiety, found that an AI 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 co-author Charles R. Marmar, Professor at NYU School of Medicine.
For the study, the research team used a statistical/Machine Learning (ML) 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.
They team involved 53 participants with PTSD ands 78 veterans without the disease.
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.
"Speech is an attractive candidate for use in an automated diagnostic system, perhaps as part of a future PTSD smartphone app, because it can be measured cheaply, remotely, and non-intrusively," said lead author Adam Brown from the varsity.
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.