ArticleQuantitative imaging in medicine and surgery2025
An intelligent quantitative analysis software for videofluoroscopic swallowing study in patients with dysphagia.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Videofluoroscopic swallowing study (VFSS) employs quantitative analysis methods, which are valued in dysphagia diagnosis for their objectivity and precision. Nonetheless, conventional methods are laborious and time-consuming. Despite advancements in automatic tracking methods, existing software still requires substantial manual intervention and offers a restricted set of quantitative metrics. This study aimed to develop an intelligent VFSS quantitative analysis tool that assists clinicians in dysphagia diagnosis by enabling automated tracking and providing a comprehensive set of three kinematic and seven temporal parameters. Methods: This software utilizes a feature-based target tracking and contour extraction algorithm, which enables accurate and automated detection of hyoid bone displacement, upper esophageal sphincter (UES) opening amplitude and pharyngeal contraction ratio. This study analyzed 82 VFSS samples from Suzhou Municipal Hospital, comprising 40 from 18 dysphagia patients with varied etiologies and 42 from 14 healthy controls. Agreement between automated and manual tracking for the three kinematic parameters was evaluated using Pearson correlation coefficients and relative errors (%). Results: In both patient and control groups, the results showed strong correlations (Pearson's r ranging from 0.947 to 0.995, P value <0.001) between automatic and manual methods across three kinematic parameters. The relative errors of three parameters of two-dimensional range were 5.30±3.79, 3.72±1.93, and 5.22±3.25 in dysphagic patients and 4.62±3.46, 3.07±2.02, and 5.43±3.69 in controls. Comparative analysis demonstrated significantly reduced values in dysphagia patients versus healthy controls across three parameters, characterizing compromised swallowing biomechanics in the dysphagia population. While manual analysis typically requires about one hour per case, the proposed platform completes automatic quantitative analysis within 3-4 minutes per sample. Conclusions: The developed software provides an efficient and user-friendly platform that streamlines dysphagia diagnosis through the automatic tracking and assessment of essential parameters, thereby enhancing the diagnostic accuracy and workflow efficiency.
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