Analyzing Facial Asymmetry in Alzheimer’s Dementia Using Image-Based Technology

• Published in: Biomedicines Vol. 11; no. 10; p. 2802
• Main Authors: Chien, Ching-Fang, Sung, Jia-Li, Wang, Chung-Pang, Yen, Chen-Wen, Yang, Yuan-Han
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023; MDPI
• Subjects: accelerated aging; Aging; Algorithms; Alzheimer's disease; Alzheimer’s dementia; Asymmetry; Brain research; Cameras; Cognitive ability; Data mining; Dementia; Dementia disorders; Disease; Facial asymmetry; facial landmarks; Hospitals; Machine learning; Procrustes method; R&D; Research & development; Taiwan
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines11102802

Several studies have demonstrated accelerated brain aging in Alzheimer’s dementia (AD). Previous studies have also reported that facial asymmetry increases with age. Because obtaining facial images is much easier than obtaining brain images, the aim of this work was to investigate whether AD exhibits accelerated aging patterns in facial asymmetry. We developed new facial asymmetry measures to compare Alzheimer’s patients with healthy controls. A three-dimensional camera was used to capture facial images, and 68 facial landmarks were identified using an open-source machine-learning algorithm called OpenFace. A standard image registration method was used to align the three-dimensional original and mirrored facial images. This study used the registration error, representing landmark superimposition asymmetry distances, to examine 29 pairs of landmarks to characterize facial asymmetry. After comparing the facial images of 150 patients with AD with those of 150 age- and sex-matched non-demented controls, we found that the asymmetry of 20 landmarks was significantly different in AD than in the controls (p < 0.05). The AD-linked asymmetry was concentrated in the face edge, eyebrows, eyes, nostrils, and mouth. Facial asymmetry evaluation may thus serve as a tool for the detection of AD.