Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps.
Clin Otolaryngol
; 49(6): 776-784, 2024 Nov.
Article
en En
| MEDLINE
| ID: mdl-39109612
ABSTRACT
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Sinusitis
/
Rinitis
/
Pólipos Nasales
/
Endoscopía
/
Aprendizaje Automático
Límite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Clin Otolaryngol
Asunto de la revista:
OTORRINOLARINGOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Rumanía