Machine Learning‐Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy
This 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.
Authors
- Henry Chu (ORCID: https://orcid.org/0000-0002-2705-0863)
- Morteza Bodaghi
- Edward D. McCoul (ORCID: https://orcid.org/0000-0003-1812-2105)
- Dipesh Gyawali (ORCID: https://orcid.org/0009-0001-3734-6000)
- Raju Gottumukkala (ORCID: https://orcid.org/0000-0003-0794-4015)
- Karen Baker (ORCID: https://orcid.org/0000-0001-7294-3516)
- Jonathan Bidwell
- Majid Hosseini
- Akio Fujiwara (ORCID: https://orcid.org/0000-0002-5640-3517)
- S.K. Bhatia
- Dhara Patel
- Thomas Mundy
- Elena Bartolone
Institutions
- Tulane University (US)
- Ochsner Health System (US)
- University of New Orleans (US)
- University of Louisiana at Lafayette (US)
Publication Details
- Journal
- International Forum of Allergy & Rhinology
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1002/alr.70045
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00