Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life.
Journal:
Computers in biology and medicine
PMID:
40037167
Abstract
BACKGROUND AND OBJECTIVE: Motor diagnosis, monitoring and management of Parkinson's disease (PD) focuses mainly on observational methods and, clinical scales, resulting in a subjective evaluation. Inertial sensors combined with artificial intelligence have emerged as a promising solution to help physicians perform early, differential, and objective quantification of motor symptoms over time. We hypothesize that a long short-term memory-deep neural network (LSTM) architecture could be an appropriate solution for producing three models to provide a holistic assessment of patients with PD from a single inertial sensor.