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Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps
Anda Gata1, Lajos Raduly2, Liviuța Budișan2
1Department of Otorhinolaryngology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj Napoca, Romania.
Summary
Artificial intelligence can predict the course of chronic rhinosinusitis with nasal polyps (CRSwNP) after surgery. Machine learning models, especially those including microRNAs, show high accuracy in forecasting disease control or relapse.
Area of Science:
- Otolaryngology
- Medical Informatics
- Genomics
Background:
- Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex inflammatory condition.
- Predicting disease course after endoscopic sinus surgery (ESS) is challenging.
- Novel predictive tools are needed to optimize patient management.
Purpose of the Study:
- To evaluate the potential of Artificial Intelligence (AI) in predicting the disease trajectory of CRSwNP.
- To develop and validate machine learning algorithms for forecasting post-ESS outcomes.
Main Methods:
- Prospective study of 85 patients undergoing first ESS for nasal polyposis.
- Collection of preoperative data (demographics, blood eosinophils, endoscopy scores, SNOT-22, PHQ-9) and follow-up data.
- Development of Random Forest algorithms using clinical data and microRNAs (miR-125b, miR-203a-3p) to predict disease control, partial control, or relapse based on POSE scores.
Main Results:
- Machine learning models accurately predicted CRSwNP disease course at 18 months post-ESS.
- Prediction accuracy ranged from 69.23% (clinical data only) to 84.62% (including microRNAs).
- miR-125b significantly enhanced algorithm accuracy and was identified as a key predictive variable.
Conclusions:
- A machine learning algorithm shows promise for predicting the disease course in CRSwNP patients.
- Inclusion of microRNAs, particularly miR-125b, improves predictive accuracy.
- This AI approach may transform the prediction and management of CRSwNP.
