Smiling difficulties in Alzheimer's disease linked to reduced nucleus accumbens and pallidum brain volume: Deep learning insights.

Abstract

Patients tend to lose the ability to smile during the course of dementia. However, such impairments have rarely been reported, likely due to challenges in quantifying facial expressions. However, feature extraction is now automated due to recent developments in deep learning, which is a machine learning method used in artificial intelligence (AI). We used the output of image-classification AI to quantify smiles in participants with Alzheimer's disease (AD) and with normal cognition (NC). We found that the ability to form a smile upon request is impaired in patients with AD and that it is associated with reduced volumes of the nucleus accumbens and pallidum. Furthermore, smiling faces were classified with higher accuracy than neutral faces in discriminating between AD and NC. A score from neutral face showed significant correlation with cognitive function. These findings generate hypotheses regarding the neural mechanisms underlying impaired facial expressions in dementia.