More than 70 per cent of adults worldwide experience a traumatic event at some point in their lives.
A new artificial intelligence tool can help diagnose post-traumatic stress disorder (PTSD) by analysing voices, a recent study has claimed.
The details were published in the Journal of Depression and Anxiety.
"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 senior study author Charles R Marmar.
More than 70 per cent of adults worldwide experiences a traumatic event at some point in their lives. Those with the condition, experience strong, persistent distress, when reminded of a triggering event.
The study authors 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.
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 rules and mathematical models that enable decision-making with increasing accuracy as the amount of training data grows.
The research team also plans to train the AI voice tool with more data, further validate it on an independent sample, and apply for government approval to use the tool clinically.
"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.
"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.
"The software analyses words - in combination with frequency, rhythm, tone, and articulatory characteristics of speech - to infer the state of the speaker, including emotion, sentiment, cognition, health, mental health and communication quality. The technology has been involved in a series of industry applications visible in startups like Oto, Ambit and Decoded Health," said Dimitra Vergyri.