Deep learning models for improving Parkinson's disease management regarding disease stage, motor disability and quality of life.

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.

METHODS

A custom dataset with 40 patients was created to train and test three deep learning models to classify PD disease stages, motor conditions and quality of life (QoL).

RESULTS

We verified an accuracy of 89 % for the disease stage classifier, 91.7 % for the motor condition classifier, and an accuracy of 87.8 % for the QoL classifier.

CONCLUSIONS

We confirmed that an LSTM architecture could produce three models to improve PD management.