Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterised by abnormal muscular activity and dream enactment in REM sleep. In its isolated form (iRBD), it is recognised as an early stage of alpha-synucleinopathies (i.e. Parkinson’s disease, dementia with Lewy bodies and multiple system atrophy). However, iRBD is often not recognised. An early, accurate, automated and population-extended recognition of iRBD, would be essential to recognise patients in early stages of alpha-synucleinopathies, enabling a timely initiation of disease modifying treatments. Furthermore, objective and automated methods would improve follow up of iRBD patients and allow personalised treatments. We aim to develop and validate a novel small, light and portable 3D video-based technology employing artificial intelligence as powerful, automatic, stand-alone instrument to identify and follow-up iRBD patients. We believe that this novel tool can revolutionise the way in which iRBD patients are identified and followed-up.
