A £2.7 million project aimed at transforming life for people living alone with dementia, is to be trialled in Cambridgeshire by Anglia Ruskin University music therapists. They will use artificial intelligence to adapt and personalise live radio to try and address the key causes of hospital admission for those suffering from dementia. Radio Me will tackle issues such as agitation and failing to take medication correctly and as a result, it is hoped quality of life will improve with people able to remain living independently at home for longer. Jörg Fachner, professor of music, Health and the brain at ARU, said: "Our role is to investigate precisely how people with dementia can benefit from this interactive radio experience. "Music therapists at ARU and partner organisations will use biomarker responses to fine-tune playlists in order to deliver emotional and cognitive stimulation, and evaluate exactly how interactive music interventions, using AI, can benefit people with dementia in their own homes and in assisted living environments." Professor Eduardo Miranda, from the University of Plymouth, added: "Radio Me builds on research carried out as part of our previous EPSRC-funded project into a brain computer music interface, as well as our work on artificial intelligence, music influencing emotion, and the University's long-running involvement in shaping national policy on dementia.
Crucial early diagnosis of dementia in general practice could improve thanks to a computer model designed in a collaboration between Brighton and Sussex Medical School (BSMS) and astrophysicists at the University of Sussex. Currently, only two-thirds of people with dementia in the UK receive a formal diagnosis, and many receive it late in the disease process, meaning that a large number are missing out on the care that could help them achieve a good quality of life. The team, led by Dr Elizabeth Ford, Senior Lecturer in Primary Care Research at BSMS, used data from GP patient records to create a list of 70 indicators related to the onset of dementia and recorded in the five years before diagnosis. Working with data scientists from astrophysics, they then tried several types of machine-learning models to identify patterns of clinical information in patient records before a dementia diagnosis. The best model was able to identify 70% of dementia cases before the GP, but also threw up a number of false positives.
However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE. Patients and methods: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively.
Doctors could use artificial intelligence to diagnose dementia more accurately and give better treatment, scientists say. Researchers have invented a computer algorithm which can analyse MRI brain scans and learn how to recognise different types of dementia. They say that although many types of the brain-destroying condition have similar symptoms, they respond differently to treatment. Being able to correctly identify which type someone has means patients could be helped earlier on in their illness or given more targeted therapy. Experts say the research is'pioneering' and has'huge potential' in the future of treating dementia, expected to affect one million Britons by 2025.