Automated detection of chewing movements in videofluoroscopic swallowing studies using deep learning for landmark detection and motion analysis.

ABSTRACT: Chewing plays a critical role in safe and effective swallowing by breaking food into manageable pieces and mixing it with saliva to form a cohesive bolus. Impairments in this process, commonly seen in older adults and individuals with neurological conditions, increase the risk of choking, aspiration, and malnutrition. Although the videofluoroscopic swallowing study (VFSS) is considered the gold standard for evaluating swallowing function, automated analysis of chewing during VFSS remains largely unexplored. Manual assessments are time-consuming, variable, and rarely integrated into standard clinical protocols. This study presents the first fully automated pipeline for analyzing jaw movements related to chewing in VFSS. The pipeline integrates three main modules: (1) a landmark detection ne

SUBMITTER: Bandini A 

PROVIDER: S-EPMC12810935 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature