Using AI technology to support ADHD diagnosis

Professor Grigoris Antoniou

NHS waiting lists for adults with ADHD (Attention Deficit Hyperactivity Disorder) continue to grow. People awaiting assessment and treatment can suffer from health, relationship and work problems. Delays in diagnosis are due to current NHS practices which require a full assessment by specialist clinicians, increased awareness of the condition and financial pressures.

Using technology to automate diagnosis

Researchers at the University of Huddersfield have developed automated reasoning techniques to deal with imperfect information, which are being applied to the diagnosis of ADHD in adults. The new technology identifies clear-cut cases that can be assessed automatically, enabling timely assessments with positive benefits on patients and NHS waiting lists.

Working with the NHS

Professor Grigoris Antoniou researches the area of nonmonotonic reasoning, a family of automated reasoning approaches within knowledge representation (KR), where the knowledge being manipulated can be inconsistent or incomplete. Prior to working with the NHS on using Artificial Intelligence (AI) to diagnose ADHD, he worked with the South-West Yorkshire Partnership NHS Foundation Trust (SWYPFT) in the area of mental health, with an initial focus on automatic risk assessment of suicide.

Following the success of this project, a new collaboration was initiated on the diagnosis of ADHD in adults, funded by SWYPFT and Research England through Grow MedTech. Professor Antoniou adapted the methodology for this project working together with clinical experts and Dr Tianhua Chen performed the analysis of data from past cases of ADHD diagnosis using machine learning with a predictive accuracy of diagnostic outcome of around 90%.

AI algorithms

Key elements of the research included using the same data that clinicians use when making a clinical decision. When addressing problems in secondary care, a referral-centric approach was found to be the most suitable. It was also necessary to apply a variety of AI algorithms to accommodate the different requirements in terms of explanation and accuracy.

The hybrid AI algorithm used both data-driven and knowledge-based models to assess the clinical data of an ADHD patient. It produced three possible outcomes: positive diagnosis, negative diagnosis or requiring further assessment by a medical specialist. The predictive accuracy based on the cases considered so far is 98%. This hybrid algorithm is now used in the adult ADHD services of SWPYFT.

Making a difference

The impact of this research is twofold, benefitting both ADHD patients and supporting the NHS.

The health and wellbeing of patients was a key focus of this research and by reducing the time it takes to diagnose and treat people with ADHD they are less likely to need time off work and risk developing other issues such as self-harm and child safeguarding.

The NHS has been able to make economic savings as no new highly specialised clinicians had to be hired. It is also expected that the NHS will benefit from being able to reduce waiting lists and meet targets more effectively, by allowing it to deploy a more flexible workforce configuration safely in the context of recruitment challenges.

For more information on the research in this article email: g.antoniou@hud.ac.uk or visit pure.hud.ac.uk