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

Autor: Aktar Ugurlu G, Ugurlu BN, Yalcinkaya M
Jazyk: angličtina
Zdroj: Aesthetic surgery journal [Aesthet Surg J] 2024 Dec 12; Vol. 45 (1), pp. NP1-NP7.
DOI: 10.1093/asj/sjae204
Abstrakt: Background: Botulinum toxin type A (BoNT-A) injections are widely administered for facial rejuvenation, but their effects on facial expressions remain unclear.
Objectives: In this study, we aimed to objectively measure the impact of BoNT-A injections on facial expressions with deep learning techniques.
Methods: One hundred eighty patients age 25 to 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 with a hybrid data set of clinical images and the Karolinska Directed Emotional Faces (KDEF) data set.
Results: The CNN model accurately predicted 90.15% of the test images. Significant decreases in the recognition of angry and surprised expressions were observed postinjection (P < .05), with no significant changes in happy or neutral expressions (P > .05). Angry expressions were often misclassified as neutral or happy (P < .05), and surprised expressions were more likely to be perceived as neutral (P < .05).
Conclusions: 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.
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Databáze: MEDLINE