Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps.

Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.

Methods: We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative (demographic data, blood eosinophiles, endoscopy, Lund-Mackay, SNOT-22 and depression PHQ scores) and follow-up data was standardly collected. Outcome measures included SNOT-22, PHQ-9 and endoscopy perioperative sinus endoscopy (POSE) scores and two different microRNAs (miR-125b, miR-203a-3p) from polyp tissue. Based on POSE score, three labels were created (controlled: 0-7; partial control: 8-15; or relapse: 16-32). Patients were divided into train and test groups and using Random Forest, we developed algorithms for predicting ESS related outcomes.

Results: Based on data collected from 85 patients, the proposed Machine Learning-approach predicted whether the patient would present control, partial control or relapse of nasal polyposis at 18 months following ESS. The algorithm predicted ESS outcomes with an accuracy between 69.23% (for non-invasive input parameters) and 84.62% (when microRNAs were also included). Additionally, miR-125b significantly improved the algorithm's accuracy and ranked as one of the most important algorithm variables.

Conclusion: We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.