Assessment of Motor Dysfunction with Virtual Reality in Patients Undergoing [¹²³I]FP-CIT SPECT/CT Brain Imaging.

Area of Science:

• Neurology
• Medical Imaging
• Biotechnology

Background:

• [¹²³I]FP-CIT SPECT imaging is established for differentiating Parkinson disease (PD) from essential tremor.
• Its utility for quantitatively assessing motor dysfunction severity remains unproven.

Purpose of the Study:

• To develop and evaluate a virtual reality (VR) application for quantifying motor dysfunction in PD patients.
• To compare the performance of VR assessment with [¹²³I]FP-CIT SPECT/CT imaging in predicting motor severity.

Main Methods:

• A VR application was used to assess bradykinesia, activities of daily living, and tremor in 44 patients with abnormal [¹²³I]FP-CIT SPECT/CT.
• Machine learning models (Support Vector Machines) were applied to both VR and SPECT data for analysis.
• Receiver operating characteristic (ROC) analysis and logistic regression were used to evaluate predictive performance.

Main Results:

• VR demonstrated a significantly higher area under the curve (AUC) (0.8418) than brain SPECT (0.5357) for detecting motor dysfunction (p=0.029).
• VR was identified as an independent predictor of motor dysfunction (Odds Ratio 326.4, p=0.008).
• Support Vector Machine models showed a higher R-squared value for VR (0.713) compared to SPECT (0.0764) in predicting UPDRS-III scores.

Conclusions:

• Virtual reality (VR) provides a safe, objective, and quantitative method for assessing motor dysfunction in Parkinson disease patients.
• VR may enhance the prediction of motor dysfunction severity, potentially serving as a valuable tool alongside or prior to [¹²³I]FP-CIT SPECT imaging.