Artificial intelligence has been taught to identify people with suicidal thoughts by analysing their brain scans, according to a US study.

Suicide is the leading cause of death in Australia among people aged 15-44, and assessing suicide risk is one of the hardest challenges for mental health clinicians.

In the study, the researchers presented 38 people who had suicidal thoughts and 41 non-suicidal people with words such as 'death', 'trouble', 'good' and 'praise' while in an MRI scanner, and a machine-learning algorithm then learned to tell the difference between the two groups.

The assessment of suicide risk is among the most challenging problems facing mental health clinicians: suicidal patients frequently disguise their intention to commit suicide, while clinicians’ predictions of suicide risk have shown to be poor.

Markers of suicide risk that do not rely on self-reports are therefore much needed.

The new study found that neural activity in response to six of the words (death, cruelty, trouble, carefree, good and praise) and in five brain locations best discriminated between the suicidal patients and controls.

The authors then trained a machine-learning algorithm to use this information to identify which participants were patients and which were controls.

The algorithm correctly identified 15 of 17 patients as belonging to the suicide group and 16 of 17 healthy individuals as belonging to the control group.

The authors went on to investigate just the suicidal patients, who were divided into two groups: those who had attempted suicide (nine participants) and those who had not (eight participants).

The authors’ algorithm correctly distinguished between suicide attempters and non-attempters in 16 out of 17 cases.

The study is accessible here.

While it is important research heading in a valuable direction, Associate Professor Sarah Whittle from the Melbourne School of Psychological Sciences at The University of Melbourne (who was not involved in the study) says it should be taken with a grain of salt.

“Just and colleagues report in new research that brain imaging techniques can be used to predict suicidal from non-suicidal young adults. The findings contribute to a growing body of research suggesting that ‘biological markers’ can be equally, if not more useful than subjective measures (for example, a patient’s own report of their feelings), in psychiatric decision making,” she said.

“The research, however, is a long way from having an impact on the actual treatment of suicidal individuals.

“For one, there were a small number of participants in the study, and most were male. Therefore, we don’t know how reliable the results might be, or if they apply to females.

“Also, the suicidal young adults were more depressed and anxious than the non-suicidal adults. So, we don’t know if the researchers’ have found biological markers of suicidality, or psychiatric problems more generally.

“If future research can show that the results are reliable, and are specific to suicidality, then it’s possible that the brain-based biological markers could be used by healthcare professionals for identification and treatment of people at risk of suicide.

“However, given that brain scans are costly, these tools are likely only to be used for the most severely mentally-ill patients.”