Artificial intelligence can predict which people who attend memory clinics will develop dementia within two years with 92 per cent accuracy, a largescale new study has concluded.
Using data from more than 15,300 patients in the US, research from the University of Exeter found that a form of artificial intelligence called machine learning can accurately tell who will go on to develop dementia.
The technique works by spotting hidden patterns in the data and learning who is most at risk. The study, published in JAMA Network Open and funded by funded by Alzheimer’s Research UK, also suggested that the algorithm could help reduce the number of people who may have been falsely diagnosed with dementia.
The researchers analysed data from people who attended a network of 30 National Alzheimer’s Coordinating Center memory clinics in the US. The attendees did not have dementia at the start of the study, though many were experiencing problems with memory or other brain functions.
In the study timeframe between 2005 and 2015, one in ten attendees (1,568) received a new diagnosis of dementia within two years of visiting the memory clinic. The research found that the machine learning model could predict these new dementia cases with up to 92 per cent accuracy – and far more accurately than two existing alternative research methods.
The researchers also found for the first time that around eight per cent (130) of the dementia diagnoses appeared to be made in error, as their diagnosis was subsequently reversed. Machine learning models accurately identified more than 80 per cent of these inconsistent diagnoses. Artificial intelligence can not only accurately predict who will be diagnosed with dementia, it also has the potential to improve the accuracy of these diagnoses.
The researchers found that machine learning works efficiently, using patient information routinely available in clinic, such as memory and brain function, performance on cognitive tests and specific lifestyle factors. The team now plans to conduct follow-up studies to evaluate the practical use of the machine learning method in clinics, to assess whether it can be rolled out to improve dementia diagnosis, treatment and care.
Dr Rosa Sancho, Head of Research at Alzheimer’s Research UK said “Artificial intelligence has huge potential for improving early detection of the diseases that cause dementia and could revolutionise the diagnosis process for people concerned about themselves or a loved one showing symptoms. This technique is a significant improvement over existing alternative approaches and could give doctors a basis for recommending life-style changes and identifying people who might benefit from support or in-depth assessments.”
The study is entitled ‘Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients’, by Charlotte James, Janice M. Ranson, Richard Everson and David J Llewellyn. It is published in JAMA Network Open.
The first author of this paper is Dr Charlotte James – Research Fellow – Charlotte is a Research Fellow in Data Science at the University of Exeter Medical School working on two projects: DEMON Network and SAMueL. Prior to joining the College of Medicine and Health, Charlotte held research positions in the Q-Step Center, University of Exeter, and the Population Health Sciences Institute, University of Bristol. Charlotte’s background is in Applied Mathematics and Computer Science and she has a PhD in Engineering Mathematics from the University of Bristol. Her current work focuses on the application of data science and AI to health research.