Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data

2026-02-19

AI Summary

This study addresses the lack of individualized predictive tools for surgical benefit in patients with chronic rhinosinusitis (CRS). Leveraging preoperative clinical data, the authors developed a supervised learning model with the SNOT-22 score as the primary outcome, achieving high accuracy and interpretability on standardized data from a prospective observational intervention trial. Using multiple algorithms and ensemble methods, the model relies solely on preoperative variables to balance clinical utility and interpretability. The optimal model attained an internal test accuracy of 85% and 80% on an external hold-out set of 30 patients, significantly outperforming the average diagnostic accuracy of expert clinicians (75.6%).

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Neuro-Symbolic Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Artificial intelligence (AI) has increasingly transformed medical prognostics by enabling rapid and accurate analysis across imaging and pathology. However, the investigation of machine learning predictions applied to prospectively collected, standardized data from observational clinical intervention trials remains underexplored, despite its potential to reduce costs and improve patient outcomes. Chronic rhinosinusitis (CRS), a persistent inflammatory disease of the paranasal sinuses lasting more than three months, imposes a substantial burden on quality of life (QoL) and societal cost. Although many patients respond to medical therapy, others with refractory symptoms often pursue surgical intervention. Surgical decision-making in CRS is complex, as it must weigh known procedural risks against uncertain individualized outcomes. In this study, we evaluated supervised machine learning models for predicting surgical benefit in CRS, using the Sino-Nasal Outcome Test-22 (SNOT-22) as the primary patient-reported outcome. Our prospectively collected cohort from an observational intervention trial comprised patients who all underwent surgery; we investigated whether models trained only on preoperative data could identify patients who might not have been recommended surgery prior to the procedure. Across multiple algorithms, including an ensemble approach, our best model achieved approximately 85% classification accuracy, providing accurate and interpretable predictions of surgical candidacy. Moreover, on a held-out set of 30 cases spanning mixed difficulty, our model achieved 80% accuracy, exceeding the average prediction accuracy of expert clinicians (75.6%), demonstrating its potential to augment clinical decision-making and support personalized CRS care.

Problem

• Chronic Rhinosinusitis
• Surgical Outcomes Prediction
• Machine Learning
• Clinical Decision-Making
• Patient-Reported Outcomes

Innovation

• machine learning
• chronic rhinosinusitis
• surgical outcome prediction
• prospective clinical data
• SNOT-22