Using Artificial Intelligence and Perception Technology to Improve the Acute Levodopa Challenge Test for Diagnosing Parkinsons Disease and Related Disorders

What this Trial Is About

Researchers are evaluating a new method that combines an innovative motor test with perception technology and artificial intelligence to improve the diagnosis of Parkinsons disease and other similar conditions. This study aims to find clear diagnostic cut-off values, explore digital markers for early and different diagnosis, and create a diagnostic model using this combined approach alongside traditional motor tests and the acute levodopa challenge test. Participants include groups with confirmed or probable Parkinsons disease, multiple system atrophy, progressive supranuclear palsy, vascular parkinsonism, drug-induced parkinsonism, corticobasal degeneration, and dementia with Lewy bodies. The study involves recording patients motor symptoms on video and assessing them with these advanced technologies and tests to better understand diagnostic accuracy. During the study, participants will undergo examinations including the acute levodopa challenge test and video recordings of their motor symptoms. Researchers will measure outcomes such as accuracy, sensitivity, specificity, predictive values, and diagnostic odds ratios to evaluate the method. The study is observational and includes evaluations to identify useful digital biomarkers and establish a reliable diagnostic model, with participants aged 50 to 75 years involved throughout the study period.

AI-Enhanced Optimization of Acute Levodopa Challenge Test