Evaluating the Impact of BoNT-A Injections on Facial Expressions: A Deep Learning Analysis.

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Abstract

Botulinum Toxin Type A (BoNT-A) injections are widely used for facial rejuvenation, but their effects on facial expressions remain unclear.This study aims to objectively measure the impact of BoNT-A injections on facial expressions using deep learning techniques.180 patients aged 25-60 years who underwent BoNT-A application to the upper face were included. Patients were photographed with neutral, happy, surprised, and angry expressions before and 14 days after the procedure. A Convolutional Neural Network (CNN)-based Facial Emotion Recognition (FER) system analyzed 1440 photographs using a hybrid dataset of clinical images and the Karolinska Directed Emotional Faces (KDEF) dataset.The CNN model accurately predicted 90.15% of the test images. Significant decreases in the recognition of angry and surprised expressions were observed post-injection (p<0.05), with no significant changes in happy and neutral expressions (p>0.05). Angry expressions were often misclassified as neutral or happy (p<0.05), and surprised expressions were more likely to be perceived as neutral (p<0.05).Deep learning can effectively assess the impact of BoNT-A injections on facial expressions, providing more standardized data than traditional surveys. BoNT-A may reduce the expression of anger and surprise, potentially leading to a more positive facial appearance and emotional state. Further studies are needed to understand the broader implications of these changes.© The Author(s) 2024. Published by Oxford University Press on behalf of The Aesthetic Society. All rights reserved. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [email protected].