Introduction

The most commonly used rating scale for monitoring Parkinson’s disease is the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale. However, this scale is limited to a 5-point system, which can restrict the detection of subtle changes in disease progression and is often subject to personal interpretation.

Innovative Approach

A research team from the University of Florida, including neurologists Joshua Wong, M.D., Nicolaus McFarland, M.D., Ph.D., and Adolfo Ramirez-Zamora, M.D., has developed a more objective method to quantify motor symptoms in Parkinson’s patients. By utilizing machine learning algorithms to analyze video footage, they can capture nuanced changes in the disease over time.

How Does It Work?

“We found that we can observe the same features that the clinicians are trying to see by using a camera and a computer,” said Guarin. The integration of AI simplifies the examination process, making it less time-consuming for all parties involved. The automated system has also uncovered previously unnoticed details about movement, such as the speed at which a patient opens or closes their fingers during movement.

New Insights

Guarin noted, “We’ve seen that, with Parkinson’s disease, the opening movement is delayed compared to healthy individuals.” This revelation highlights the potential of technology to provide new markers for evaluating therapy effectiveness.

Utilizing AI Supercomputers

To enhance the system, Guarin initially designed it to analyze facial features for other conditions. The team utilized UF’s HiPerGator, one of the largest AI supercomputers globally, to train their models. “HiPerGator enabled us to develop a machine learning model that simplifies the video data into a movement score,” Guarin explained.

Implications for Clinical Trials

Michael S. Okun, M.D., director of the Norman Fixel Institute and medical advisor for the Parkinson’s Foundation, stated that these automated video assessments could be a “game changer” for both clinical trials and patient care. “The finger-tapping test is critical for diagnosis and measuring disease progression,” Okun added.

Future Developments

In addition to providing this technology to neurologists and care providers, Guarin is collaborating with UFIT to develop a mobile app that will allow individuals to assess their disease progression from home.

References

Reference: Guarín DL, Wong JK, McFarland NR, Ramirez-Zamora A. Characterizing disease progression in Parkinson’s disease from videos of the finger tapping test. IEEE Trans Neural Syst Rehabil Eng. 2024;32:2293-2301. doi: 10.1109/TNSRE.2024.3416446

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