AI ‐Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention‐Based Modeling

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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 p = 0.027). In poor‐quality images, the model correctly segmented 89.3% of mucus regions, with improved boundary detection in cases of irregular mucus patterns and reduced false positives caused by glare and overexposure. The system's targeted architectural components address specific diagnostic challenges while maintaining real‐time processing at 17.3 frames per second. 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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