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To this end, this article proposes a multimodal nasal obstruction degree classification model based on cone beam computed tomography (CBCT) images and nasal resistance measurements. The model consists of four modules: image feature extraction, table feature extraction, feature fusion, and classification. In the image feature extraction module, this article proposes a strategy of using the trained MedicalNet large model to get the pre\u2010training parameters and then migrating them to the three\u2010dimensional convolutional neural network (3D CNN) feature extraction model. For the nasal resistance measurement form data, a method based on extreme gradient boosting (XGBoost) feature importance analysis is proposed to filter key features to reduce the data dimension. In order to fuse the two types of modal data, a feature fusion method based on local and global features was designed. Finally, the fused features are classified using the tabular network (TabNet) model. In order to verify the effectiveness of the proposed method, comparison experiments and ablation experiments are designed, and the experimental results show that the accuracy and recall of the proposed multimodal classification model reach 0.93 and 0.9, respectively, which are significantly higher than other methods.<\/jats:p>","DOI":"10.1111\/nyas.70085","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T17:54:26Z","timestamp":1759514066000},"page":"541-556","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multimodal Classification Method for Nasal Obstruction Severity Based on Computed Tomography and Nasal Resistance"],"prefix":"10.1111","volume":"1553","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8148-9819","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"first","affiliation":[{"name":"Shengjing Hospital of China Medical University  Shenyang 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