The challenge

Diagnosing mental health disorders

When diagnosing mental health disorders such as depression and bipolar disorder, clinicians around the world mainly rely on the Diagnostic and Statistical Manual (DSM) and the International Classification of Diseases (ICD), which provide a specific set of criteria for diagnosis. However, 69 per cent of bipolar patients are initially misdiagnosed, with around one-third of these patients remaining misdiagnosed for 10 years or more, potentially leading to undue stress or harm.

Our response

Computer games to analyse neural activity

By developing an interactive computer game, clinicians have been given the ability to see a patient's brain processes in response to tailored stimuli, unlike traditional mental health assessments which only measure a patient's response to direct questions about their mental state.

The computer game presents individuals with two choices and tracks their behaviour as they respond. The complex data collected from the game is analysed through artificial neural networks — brain-inspired systems intended to replicate the way that humans learn — which can disentangle the nuanced behavioural differences between healthy individuals, and those with depression or bipolar disorder.

In a study of 101 participants - 34 with depression, 33 with bipolar disorder, and a control group of 34 subjects - artificial intelligence techniques deployed through the computer game were able to identify behavioural patterns in subjects with depression and bipolar disorder, down to subtle individual differences in each group.

Dr Amir Dezfouli, neuroscientist and machine learning expert at CSIRO’s Data61, with the simple computer game  ©

The results

Personalising unique treatment plans

By understanding how the brain works, more accurate processes for diagnosis can be developed, and characterising mental health disorders in granular detail could allow clinicians to develop more personalised treatment plans based on an individual's unique diagnosis. This technique could build on the DSM, and provide an additional decision-making tool for clinicians, leading to better patient outcomes.

Currently, the researchers are looking to partner with hospitals and mental health research centres to conduct further research to validate the technique for real-world use, providing decision support for clinicians.

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