ArticleFrontiers in radiology2026
YOLO11-based detection of manometry sensors in video-fluoroscopy imaging for computer-aided multimodal assessment of swallowing.
Article in Frontiers in radiology, 2026. 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
Purpose: Accurate assessment of swallowing function is essential in the diagnosis and monitoring of dysphagia following head and neck cancer (HNC). The simultaneous analysis of video-fluoroscopy swallow studies (VFSS) and high-resolution impedance manometry (HRIM) offers a more comprehensive evaluation, reducing subjectivity in VFSS and improving anatomical context of HRIM in HNC patients. Methods: The inherently low pharyngeal pressures in post-treatment HNC patients hinder the analysis of HRIM. As such, this study proposes a deep learning method for the automatic detection of HRIM sensors in VFSS using a YOLO11-based detector, aimed at enabling the automatic delineation of manometric regions. Detection performance was evaluated on 268 frames from 8 HNC patients using a leave-one-patient-out cross-validation approach. EigenCAM-based heatmaps were produced to analyze the model's attention patterns. Results: The model achieved 95.8% Precision, 97.4% Recall, 96.6% F1-score with minimal variation between folds. Under different noise levels and bolus-simulated obstructions, performance remained robust. Our method outperformed previous template-matching methods for manometric sensor detection in VFSS. EigenCAM visualizations confirmed consistent attention to catheter regions. Conclusion: The proposed YOLO11-based detector provides accurate and robust localization of manometric sensors in VFSS sequences to facilitate computer-assisted HRIM-VFSS fusion for objective swallowing assessment.
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