Multimodal framework for swallow detection in video-fluoroscopic swallow studies using manometric pressure...

Oropharyngeal dysphagia affects up to half of head and neck cancer (HNC) patients. Multi-swallow video-fluoroscopic swallow studies (VFSS) combined with high-resolution impedance manometry (HRIM) offer a comprehensive assessment of swallowing function. However, their use in HNC populations is limited by high clinical workload and complexity of data collection and analysis with existing software.

To address the data collection challenge, we propose a framework for automatic swallow detection in simultaneous VFSS-HRIM examinations. The framework identifies candidate swallow intervals in continuous VFSS videos using an optimized double-sweep optical flow algorithm. Each candidate interval is then classified using a pressure-based swallow template derived from three annotated samples, leveraging features such as normalized peak-to-peak amplitude, mean, and standard deviation from upper esophageal sphincter sensors.

The methodology was evaluated on 97 swallows from twelve post-head and neck cancer patients. The detection pipeline achieved 95% Recall and 92% F1-score. Importantly, the number of required HRIM annotations was reduced by 63%, substantially decreasing clinician workload while maintaining high accuracy.

This framework overcomes limitations of current software for simultaneous VFSS-HRIM collection by enabling high-accuracy, low-input swallow detection in HNC patients. Validated on a heterogeneous patient cohort, it initiates the groundwork for scalable, objective, and multimodal swallowing assessment.