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

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-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0<middle dot>94, sensitivity 0<middle dot>96, and specificity 0<middle dot>8 for the internal testing subset; AUC 0<middle dot>85, sensitivity 0<middle dot>87, and specificity 0<middle dot>82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0<middle dot>79 vs 0<middle dot>71, sensitivity 0<middle dot>88 vs 0<middle dot>81, specificity 0<middle dot>75 vs 0<middle dot>68, p < 0<middle dot>05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance. Interpretation The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.