Intracerebral Hemorrhage

Harrigan, Mark R.; Deveikis, John P.

doi:10.1007/978-3-319-66779-9_17

Cited by 1 publication

(1 citation statement)

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“…The majority of ICH cases are attributed to ruptures of arteriole with diameters ranging from 50 to 200 micrometers. [ 40 ], Studies have indicated that chronic hypertension may be a significant factor contributing to arteriole ruptures [ 41 ]. Following a cerebral hemorrhage event, the primary cause of early neural damage is attributed to increased intracranial pressure, leading to compression and disruption of brain tissue structure [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study

Zhang,

Yang,

Song

et al. 2024

BMC Med Imaging

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Objectives To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. Materials and methods Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. Results The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. Conclusion The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. Clinical relevance statement Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

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Section: Discussionmentioning

confidence: 99%