AI-PROGNOSIS aims to build a system of digital biomarkers (dBMs) to support continuous monitoring of Parkinson’s disease (PD) and to inform predictive models of disease risk and prognosis. The report presents the first version of these dBMs, focusing on both motor and non-motor symptoms that are central to PD progression.

The initial set of digital biomarkers draws on data from wearable devices, smartphone interactions, and digital active tests, capturing a broad spectrum of PD-related features, including:

• Motor symptoms: rest tremor, slowness of movement (bradykinesia), dyskinesias, gross motor function

• Non-motor symptoms: REM Behaviour Disorder (RBD), daytime somnolence, physical activity, cognitive function

  
  

This early development phase lays the groundwork for more refined models that will help clinicians and researchers track symptom progression remotely and more objectively, ultimately aiming to improve personalised care and prognosis in Parkinson’s disease.

Read the full report on Zenodo: https://zenodo.org/records/15542385