{"id":1230,"date":"2026-01-25T19:32:17","date_gmt":"2026-01-25T19:32:17","guid":{"rendered":"https:\/\/www.cs.ubbcluj.ro\/~meco\/machine-learning-model-predicts-postoperative-outcomes-in-chronic-rhinosinusitis-with-nasal-polyps-2024\/"},"modified":"2026-02-01T12:07:46","modified_gmt":"2026-02-01T12:07:46","slug":"machine-learning-model-predicts-postoperative-outcomes-in-chronic-rhinosinusitis-with-nasal-polyps-2024","status":"publish","type":"post","link":"https:\/\/www.cs.ubbcluj.ro\/~meco\/machine-learning-model-predicts-postoperative-outcomes-in-chronic-rhinosinusitis-with-nasal-polyps-2024\/","title":{"rendered":"Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps (2024)"},"content":{"rendered":"
\n

Clinical Otolaryngology<\/p>\n

Authors<\/h2>\n

Anda G\u00e2ta, L. Raduly, Liviu\u021ba Budi\u0219an, Ad\u00e9l Bajcsi, Teodora-Maria Ursu, C. Chira, Laura-Silvia Dio\u015fan, I. Berindan\u2010Neagoe, S. Albu<\/p>\n

Abstract<\/h2>\n

Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Conclusion: We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.<\/p>\n

Citation<\/h2>\n
@Inproceedings{G\u00e2ta2024MachineLM,\n author = {Anda G\u00e2ta and L. Raduly and Liviu\u021ba Budi\u0219an and Ad\u00e9l Bajcsi and Teodora-Maria Ursu and C. Chira and Laura-Silvia Dio\u015fan and I. Berindan\u2010Neagoe and S. Albu},\n booktitle = {Clinical Otolaryngology},\n title = {Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps},\n year = {2024}\n}<\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.<\/p>\n
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.<\/p>\n
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.<\/p>\n
Conclusion: We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[78,40,11],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1230"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/comments?post=1230"}],"version-history":[{"count":1,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1230\/revisions"}],"predecessor-version":[{"id":1450,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1230\/revisions\/1450"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media?parent=1230"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/categories?post=1230"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/tags?post=1230"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}