Abstract
Botulinum neurotoxin (BoNT) treatment outcomes are commonly assessed through visual evaluation of facial wrinkle patterns, a process that remains inherently subjective despite structured grading systems. This study evaluated whether contemporary multimodal artificial intelligence (AI) systems can identify facial changes associated with BoNT treatment, using region-specific wrinkle patterns as surrogate markers of underlying muscle activity. A dataset of 46 facial images (23 pre-treatment, 23 post-treatment) was analyzed using four multimodal models, each assessed across five independent runs. Models were tasked with classifying treatment state from single images, detecting wrinkle presence in the forehead, glabella, and periorbital regions, and generating exploratory severity scores and age estimates. Two models achieved 100% accuracy in distinguishing pre- from post-treatment images in this dataset, while region-specific wrinkle detection was variable and frequently did not exceed majority-class baselines. Inter-run reliability varied substantially across models. Exploratory wrinkle severity scores showed directional differences between treatment states, whereas apparent age estimates demonstrated minimal systematic variation. These findings suggest that global facial changes associated with BoNT treatment appear to be detectable in model outputs, but region-specific assessment remains limited, underscoring the need for cautious interpretation and further validation.