Automated dysphagia characterization in head and neck cancer patients using videofluoroscopic swallowing studies

Dysphagia is one of the most common toxicities following head and neck cancer (HNC) radiotherapy (RT). Videofluoroscopic Swallowing Studies (VFSS) are the gold standard for diagnosing and assessing dysphagia, but current evaluation methods are manual, subjective, and time-consuming. This study introduces a novel framework for the automated analysis of VFSS to characterize dysphagia in HNC patients.

The proposed methodology integrates three key steps: (i) a deep learning-based labeling framework, trained iteratively to identify ten regions of interest; (ii) extraction of 23 swallowing dynamic parameters, followed by comparison across diverse cohorts; and (iii) machine learning (ML) classification of the extracted parameters into four dysphagia-related impairments.

The labeling framework achieved high accuracy, with a mean error of 1.6 pixels across the ten regions of interest in an independent test dataset. Analysis of the extracted parameters revealed significant differences in swallowing dynamics between healthy individuals, HNC patients before and after RT, and patients with non-HNC-related dysphagia. The ML classifiers achieved accuracies ranging from 0.60 to 0.87 for the four dysphagia-related impairments.

Despite challenges related to dataset size and VFSS variability, our framework demonstrates substantial potential for automatically identifying ten regions of interest and four dysphagia-related impairments from VFSS. This work sets the foundation for future research aimed at refining dysphagia analysis and characterization using VFSS, particularly in the context of HNC RT.