Anda Gata, Lajos Raduly, Liviuța Budișan, Adél Bajcsi, Teodora-Maria Ursu, Camelia Chira, Laura Dioșan, Ioana Berindan-Neagoe, Silviu Albu
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Additionally, miR-125b significantly improved the algorithm's accuracy and ranked as one of the most important algorithm variables.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.</p>\n </section>\n </div>","PeriodicalId":10431,"journal":{"name":"Clinical Otolaryngology","volume":"49 6","pages":"776-784"},"PeriodicalIF":1.7000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coa.14208","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps\",\"authors\":\"Anda Gata, Lajos Raduly, Liviuța Budișan, Adél Bajcsi, Teodora-Maria Ursu, Camelia Chira, Laura Dioșan, Ioana Berindan-Neagoe, Silviu Albu\",\"doi\":\"10.1111/coa.14208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. 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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.
期刊介绍：
Clinical Otolaryngology is a bimonthly journal devoted to clinically-oriented research papers of the highest scientific standards dealing with:
current otorhinolaryngological practice
audiology, otology, balance, rhinology, larynx, voice and paediatric ORL
head and neck oncology
head and neck plastic and reconstructive surgery
continuing medical education and ORL training
The emphasis is on high quality new work in the clinical field and on fresh, original research.
Each issue begins with an editorial expressing the personal opinions of an individual with a particular knowledge of a chosen subject. The main body of each issue is then devoted to original papers carrying important results for those working in the field. In addition, topical review articles are published discussing a particular subject in depth, including not only the opinions of the author but also any controversies surrounding the subject.
• Negative/null results
In order for research to advance, negative results, which often make a valuable contribution to the field, should be published. However, articles containing negative or null results are frequently not considered for publication or rejected by journals. We welcome papers of this kind, where appropriate and valid power calculations are included that give confidence that a negative result can be relied upon.
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