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Related Experiment Video

Updated: Jun 24, 2026

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols
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AI-Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention-Based Modeling.

Dipesh Gyawali1, Jonathan Bidwell1, Elena Karras2

  • 1Department of Otorhinolaryngology Ochsner Health New Orleans Louisiana USA.

Laryngoscope Investigative Otolaryngology
|June 15, 2026
PubMed
Summary

SUM-MucusNet, an AI model, improves mucus segmentation in nasal endoscopy images, overcoming challenges like poor quality and glare. This aids sinusitis diagnosis by providing more accurate mucus identification.

Keywords:
artificial intelligencecomputer‐assisted diagnosismucus segmentationnasal endoscopysinusitis

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Area of Science:

  • Medical Computer Vision
  • Artificial Intelligence in Medicine
  • Otolaryngology Imaging

Background:

  • Accurate mucus segmentation in nasal endoscopy is crucial for sinusitis diagnosis.
  • Existing models face challenges with thin mucus, boundary delineation, and illumination artifacts.

Purpose of the Study:

  • Introduce SUM-MucusNet, an AI model to enhance mucus identification in challenging nasal endoscopy images.
  • Improve the accuracy and reliability of mucus segmentation for clinical diagnosis.

Main Methods:

  • Trained SUM-MucusNet, a deep learning model, on 3486 annotated endoscopy images from 454 patients.
  • Evaluated performance using Dice coefficient, IoU, precision, and recall against expert observations and existing models.

Main Results:

  • SUM-MucusNet achieved a Dice coefficient of 0.71, a statistically significant improvement over prior models.
  • Correctly segmented 89.3% of mucus in poor-quality images, with better boundary detection and reduced false positives from glare/overexposure.
  • The model operates in real-time at 17.3 frames per second.

Conclusions:

  • SUM-MucusNet enhances mucus segmentation in nasal endoscopy by addressing image quality, boundary, and illumination issues.
  • Advances medical computer vision in an under-explored area, supporting quantitative mucus assessment.
  • Facilitates standardized diagnosis and longitudinal disease monitoring in nasal endoscopy.