ArticleLaryngoscope investigative otolaryngology2026
AI-Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention-Based Modeling.
Article in Laryngoscope investigative otolaryngology, 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
Objective: Mucus segmentation in nasal endoscopy is essential for sinusitis diagnosis, yet existing models struggle with separating thin mucus layers and serous secretions, delineating boundaries, and handling illumination artifacts like overexposure and reflections. We introduce SUM-MucusNet, an artificial intelligence model designed to assist clinicians in identifying mucus across challenging settings. Methods: We trained SUM-MucusNet, a deep learning model, on 3486 annotated endoscopy images from 454 patients. Performance was measured against expert physician observations using the Dice coefficient, Intersection over Union (IoU), precision and recall, and the comparative analysis was performed with the existing models. Results: SUM-MucusNet achieved a Dice coefficient of 0.71 (95% CI: 0.70-0.72), representing a modest but statistically supported improvement over the next-best model (Wilcoxon signed-rank Conclusion: This work improves mucus segmentation in diagnostic nasal examinations by addressing image quality, boundary irregularity, and illumination challenges, advancing an under-explored area in medical computer vision to support standardized, quantitative mucus assessment and longitudinal disease monitoring during diagnostic nasal endoscopy. Level of Evidence: 2.
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