This week, Dr Matias Rusane interviews Dr Matteo Cesari, a leading expert in the application of artificial intelligence to REM sleep behaviour disorder (RBD). Following the current theme “Sleep in the age of technology” of the ECN committee, they discussed the impact of AI on RBD research.

RBD is a parasomnia characterised by abnormal muscle activity and dream enactment during REM sleep. The isolated form of the disorder (iRBD) is now widely recognised as an early stage of alpha-synuclein-related neurodegenerative diseases, such as Parkinson’s disease. Studies demonstrate that 80–90% of individuals diagnosed with iRBD develop neurodegenerative symptoms within 10-15 years. As such, this population represents an ideal cohort for clinical trials aimed at testing disease-modifying therapies.

Recent advances suggest that AI could play a transformative role in several key areas of RBD research and clinical management:

– Diagnosis: Current diagnostic guidelines rely on labour-intensive manual and visual analysis of polysomnographic data to detect abnormal muscle tone and behaviours during REM sleep. AI offers a promising alternative by enabling faster, automated, and objective assessment, thereby supporting clinicians in making more efficient and accurate diagnoses.

– Screening: At present, RBD diagnosis is limited to specialised neurological sleep laboratories, contributing to widespread underdiagnosis. However, the integration of AI with emerging wearable technologies could enable large-scale screening in the general population. This capability will become increasingly important as disease-modifying treatments become available, highlighting the need for early detection.

– Prognosis: AI is also being explored for its ability to detect complex patterns across sleep recordings, brain imaging, and biomarker data. These insights may help predict the timeline for progression from iRBD to overt alpha-synucleinopathies. In turn, this predictive power could allow for more personalised treatment planning and early intervention strategies.

In conclusion, advancements in AI hold great promise for transforming RBD research and clinical care. Through collaborative efforts across institutions and disciplines, the field is well-positioned to develop innovative tools that enhance diagnosis, enable large-scale screening, and improve prognostic accuracy. These developments have the potential to significantly impact clinical practice and patient care.

In the interview, Dr. Cesari also shares his advice for early career professionals who want to work with AI in sleep.