International journal of computer assisted radiology and surgery · 2025
Multimodal framework for swallow detection in video-fluoroscopic swallow studies using manometric pressure distributions from dysphagic patients.
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da Rocha Manuel Maria Loureiro, van der Molen Lisette, Neijman Marise, van Alphen Marteen J A, van den Brekel Michiel M W M, Siepel Françoise J
Аннотация
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.