Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications

Abstract

In cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial attractiveness. The aim of this work is to promote communications between the two parties by providing a quadruple of quantitative measurements: overall asymmetry index (oAI), asymmetry vector, classification, and confidence vector, using an artificial neural network classifier to model people’s perception acquired from visual questionnaires concerning facial asymmetry. The questionnaire results exhibit a Cronbach’s Alpha value of 0.94 and categorize the respondents’ perception of each stimulus face into perceived normal (PN), perceived asymmetrically normal (PAN), and perceived abnormal (PA) categories. The trained classifier yields an overall root mean squared error < 0.01, and its result shows that the oAI is, in general, proportional to the degree of perceived asymmetry. However, there exist faces that are difficult to classify as either PN or PAN or either PAN or PA with competing confidence values. In such cases, oAI alone is not sufficient to articulate facial asymmetry. Assisting surgeon–patient conversations with the proposed asymmetry quadruple is advised to avoid or to mitigate potential medical disputes.

Keywords

• artificial neural networks
• facial asymmetry
• plastic surgery
• medical disputes