Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT) plays an important role in the diagnosis, treatment, and research of cerebrovascular diseases. Non-contrast CT is widely used in the clinical diagnosis of ICH because of its high imaging speed and high sensitivity and specificity in the detection of stroke. Many markers-based CT imaging, quantitative parameters, and artificial intelligence (AI) methods based on CT are increasingly used for the prediction of hematoma expansion (HE), prognosis of ICH, and the evaluation of perihematomal edema (PHE). Therefore, we performed a comprehensive review of studies, focusing on current research evidence related to CT use for the prediction of HE and prognostic. This review discusses recent insights into, outlines current limitations, and puts forward suggestions for the challenges and directions of future research. Although at present the prognosis for ICH is not optimistic, the treatment methods remain controversial. However, identifying imaging markers that can evaluate and predict existing possible existing therapeutic targets could help to provide individualized advice for patients and achieve patient risk stratification, which is a key step in improving treatment outcomes.
Keywords: Artificial intelligence; Computed tomography; Intracerebral hemorrhage; Perihematomal edema.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.