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Due to its rapid imaging advantages, CT becomes the preferred choice for cerebral herniation screening. With the continuous development of artificial intelligence technology in the field of neurological diseases, CT\u2010based models provide significant support for computer\u2010aided clinical diagnosis. However, current research on cerebral herniation diagnosis remains limited. Existing methods rely on traditional machine learning or focus solely on midline shift detection, which not only exhibits strong subjectivity but also neglects key structures such as the brainstem and the rich information from sagittal CT images. To address these limitations, this study focuses on mid\u2010sagittal CT images including the brainstem and combines clinical data to construct a multimodal deep learning framework for cerebral herniation prediction. The model integrates mature and advanced deep learning architectures to extract and fuse features from CT images and clinical text data, employing multiscale convolution and attention mechanisms for diagnostic classification. The model is evaluated on datasets from two centers. Results show that on the internal test set, the model achieves accuracy, sensitivity, specificity, and AUC of 89%, 92%, 88%, and 0.94, respectively; on the external test set, it attains accuracy, sensitivity, specificity, and AUC of 81%, 82%, 80%, and 0.89, respectively, outperforming baseline methods and existing state\u2010of\u2010the\u2010art approaches. Additionally, when compared with radiologists on the internal test set, the model\u2019s performance matches or exceeds the consensus of physicians. We also reveal the model\u2019s focus region through visual analysis, which further deepens the understanding of the model\u2019s prediction process and enhances its interpretability. Experiments demonstrate that the proposed method holds significant potential in assisting cerebral herniation diagnosis.<\/jats:p>","DOI":"10.1155\/int\/9369999","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T08:20:01Z","timestamp":1762849201000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Deep Learning for Predicting Cerebral Herniation Using Sagittal CT and Clinical Data"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8137-6242","authenticated-orcid":false,"given":"Like","family":"Ji","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3456-927X","authenticated-orcid":false,"given":"Fuxing","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3014-5667","authenticated-orcid":false,"given":"Zicheng","family":"Xiong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9565-2285","authenticated-orcid":false,"given":"Jun","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5673-8870","authenticated-orcid":false,"given":"Fang","family":"Zuo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7493-1081","authenticated-orcid":false,"given":"Kefan","family":"Yi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9026-8128","authenticated-orcid":false,"given":"Shengbo","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5369-7158","authenticated-orcid":false,"given":"Wenying","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9235-728X","authenticated-orcid":false,"given":"Kai","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3905-4220","authenticated-orcid":false,"given":"Ghulam","family":"Mohi-ud-din","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2019190018"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/neuros\/nyaa278"},{"key":"e_1_2_10_3_2","doi-asserted-by":"crossref","unstructured":"TsiourisA. 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