Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos

TL;DR

This paper introduces a deep learning video analysis framework for assessing Parkinson's disease severity using sit-stand videos, achieving higher accuracy than clinicians in evaluating key UPDRS sub-scores.

Contribution

The study presents a novel, marker-less, video-based deep learning method for automated UPDRS assessment, enabling remote and passive monitoring of Parkinson's disease.

Findings

Framework outperforms clinicians in F1-score for bradykinesia and PIGD

Uses sit-to-stand activity for UPDRS sub-score evaluation

Potential for at-home and telemedicine PD monitoring

Abstract

This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinsons disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine.