ArticleInternational forum of allergy & rhinology2026
Machine Learning-Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy.
Article in International forum of allergy & rhinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- AI-Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention-Based Modeling.Laryngoscope investigative otolaryngology · 2026Article
- Machine Learning-Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy.International forum of allergy & rhinology · 2026Article
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13 authors.
Funding
Abstract
backgroundSinusitis is a prevalent disease for which nasal endoscopy (NE) is an optimal diagnostic modality. However, NE accuracy is limited by inter-operator variability in landmark identification and localization of mucus that is necessary for sinusitis diagnosis. We sought to develop a novel multi-class machine learning (ML) framework that detects anatomical landmarks and structures for sinusitis assessment as supported by clinical best practices.
methodsA total of 3513 NE images from 452 patients were manually annotated by four physicians for three classes: middle turbinate (MT), inferior turbinate (IT), and mucus. A YOLOv11-nano model was trained for multi-class detection and segmentation. We developed a rule-based logic for middle meatus localization, implementing a clinical algorithm that applies anatomy Intersection over Union (IoU) and conditional logic for sinusitis diagnosis. The system was validated on 178 images from 50 patients with chronic rhinosinusitis without polyps (CRSsNP) with benchmarking of real-time performance.
resultsThe multi-class detection and segmentation model achieved > 75% F1 score for detecting turbinates with mucus. The clinical algorithm achieved 75.0% sensitivity, 76.0% specificity, and 75.2% accuracy for sinusitis classification, with a F1 score of 81.8%, approaching the accuracy of a trained otolaryngologist. The framework achieved near real-time performance at > 20fps on GPU device, demonstrating suitability for integration into live clinical workflows.
conclusionThis novel ML-driven diagnostic framework with a rule-based clinical algorithm enhances decision-making for diagnosing sinusitis with NE. By reducing inter-operator variability, achieving performance comparable to otolaryngologists, and enabling real-time processing for non-specialists, this work holds potential for standardizing care and improving patient outcomes. Future research will focus on expanding to different sinusitis phenotypes and prospective real-time implementation in clinical settings.
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