Multimodal Artificial Intelligence-Based Fall Risk Prediction in Patients With Parkinson's Disease: Single vs. Dual-Task Conditions

Detailed Summary

Parkinson's disease (PD) is characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, often leading to gait disturbances and a high risk of falls. Dual-task walking assessments-requiring simultaneous motor and cognitive engagement-have gained importance in evaluating real-life mobility impairments in PD, as they more accurately reflect challenges faced during daily activities. While clinical tools such as the Timed Up and Go (TUG), Four Square Step Test (FSST), and Mini-BESTest are widely used, their in-person application may not always be feasible for individuals with mobility or access limitations. Telehealth-based assessment methods, therefore, offer practical alternatives. Recently, the integration of artificial intelligence (AI), particularly machine learning (ML), into clinical assessments has opened new possibilities for fall risk prediction by enabling the simultaneous analysis of motor, cognitive, and balance-related parameters. This study aims to predict fall risk in individuals with PD using AI-based models that incorporate multiple data sources. Furthermore, it compares the predictive accuracy of models derived from single-task and dual-task conditions, with the goal of developing a more precise and clinically useful decision-support tool for early intervention.

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