Clinical Research Directory

A Computer-Assisted Tool for Objective Assessment of Extrapyramidal Symptoms

This prospective, single-center observational study aims to develop and validate a computer-assisted tool for the objective quantitative assessment of extrapyramidal symptoms (EPS). The study will enroll 180 participants, including 90 healthy controls and 90 participants with clinically confirmed antipsychotic-induced EPS. The healthy control group will provide reference data on normal movement, while the EPS disease group will provide data on abnormal movements associated with drug-induced parkinsonism, akathisia, or tardive dyskinesia. Each participant will complete one study visit lasting approximately 60 minutes. During the visit, a trained psychiatrist will assess EPS using the Simpson-Angus Scale (SAS), Barnes Akathisia Rating Scale (BARS), and Abnormal Involuntary Movement Scale (AIMS). Participants will also complete standardized movement tasks while a non-contact computer vision system records and analyzes movement features. The device-based assessment will be repeated after a short rest to evaluate the stability of the measurements. Data collected earlier in the study will be used to develop and optimize the assessment tool. After the algorithm is finalized and locked, data from subsequently enrolled participants will be used for independent validation. The study will evaluate how closely the tool's results agree with clinician assessments, as well as its ability to identify EPS, produce stable repeated measurements, and operate feasibly and safely in a clinical setting. This is an observational study. The study does not assign treatment, change participants' medications, or use the investigational tool to make treatment decisions.

AI-Enhanced Optimization of Acute Levodopa Challenge Test

A quantitative evaluation method was developed for Parkinson's disease and other atypical parkinonism by integrating an innovative motor paradigm with perception technologies and artificial intelligence. Combined with traditional motor paradigms and the acute levodopa challenge test, this study aims to identify diagnostic cut-off values for PD and other atypical parkinonism, explore digital biomarkers for early and differential diagnosis, and establish a corresponding diagnostic model.