Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography

作者：Kai-Cheng Yang, Yunzhi Xu, Qiang Li, Lili Tang, Jiawei Zhong, Hongna An, Y. Zeng, Ke Jia, Yujia Jin, Guoshen Yu, Feng Gao, Li Zhao, Lusha Tong · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103128 · 被引用次数：6 · 研究领域：Cerebral Venous Sinus Thrombosis、Intracerebral and Subarachnoid Hemorrhage Research、Venous Thromboembolism Diagnosis and Management

Background Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT). Methods The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023 at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention. Findings An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-...