NeuralCipher Research overview
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Abstract Hypomimia, characterized by reduced facial expression, is a cardinal feature of Parkinson's Disease (PD). However, unlike limb asymmetry in PD, facial asymmetry has been less explored. Here, we explore possible subtle hemihypomimia in PD using Artificial Intelligence (AI) and image processing techniques. After video preprocessing facial expression videos from 102 PD subjects and 97 healthy controls (HCs), asymmetry index values across facial landmarks were calculated for each frame. Dynamic features were extracted and used in machine learning models to differentiate between PD and HCs, achieving 91.4% accuracy. PD subjects showed greater facial asymmetry, particularly around the eyebrows (P = 0.01) and mouth (P = 0.04), and those with asymmetric limb Parkinsonism exhibited less facial mobility on the more affected side (P = 0.001). These findings support the presence of facial expression asymmetry in PD, particularly during expressions of happiness, and suggest its potential as a clinical digital biomarker.
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