ArticleHead & face medicine2025
Development of a machine learning-based predictive model for maxillary sinus cysts and exploration of clustering patterns.
Article in Head & face medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A Retrospective Study of the Prevalence of Maxillary Sinus Cysts Incidentally Detected on MRI Among Non-Symptomatic Caucasian Population.Journal of clinical medicine · 2025Article
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6 authors.
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Abstract
BACKGROUND AND
objectiveThere are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern by cone beam computerized tomography (CBCT) technique and machine learning (ML) method to provide a theoretical basis for the prevention and clinical management of maxillary sinus cyst.
methodsIn this study, 6000 CBCT images of maxillary sinus from 3093 patients were evaluated to document the possible influencing factors of maxillary sinus cysts, including gender, age, odontogenic factors, and anatomical factors. First, the characteristic variables were screened by multiple statistical methods, and ML methods were applied to construct a prediction model for maxillary sinus cysts. Second, the model was interpreted based on the SHapley Additive exPlanations (SHAP) values, and the risk of maxillary sinus cysts was predicted by generating a web page calculator. Finally, the K-mean clustering algorithm further identified risk factors for maxillary sinus cysts.
resultsBy comparing the various metrics in the training and test sets of multiple ML models, eXtreme Gradient Boosting (XGBoost) is the best model. The average area under curve (AUC) values of the XGBoost model in the training, validation, and test sets, respectively, are 0.939, 0.923, and 0.921, which indicates its excellent classification and discrimination ability. The cluster analysis model further categorized maxillary sinus cysts into high-risk and low-risk groups, with apical lesions, severe periodontitis, and age ≥ 53 as high-risk factors for maxillary sinus cysts.
conclusionThese findings provide valuable insights into the etiology and risk stratification of maxillary sinus cysts, offering a theoretical basis for their prevention and clinical management. The integration of CBCT imaging and ML techniques holds the potential for prevention and personalized treatment strategies of maxillary sinus cysts.
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