Abstract
Objective This study aimed to explore the application value of transcranial doppler (TCD) in the inspection of cerebral vasospasm (CVS) after the treatment of intracranial aneurysm.
Methods: 105 cases of patients with confirmed intracranial aneurysm were divided into two groups based on the two different treatments - craniotomy and aneurysmal clipping or interventional embolization therapy. TCD was applied to monitor the conditions of CVS of 105 cases, and case study research method was used to analyze and conclude the TCD inspection data of patients with intracranial aneurysm detected after operation.
Results: The sensitivity of TCD in the detection of CVS was 83% and the specificity was 88%. Further, the incidence rate of CVS in the group treated with interventional embolization therapy was higher than that of the group treated with aneurysm clipping.
Conclusions: TCD, which can be used to guide the adjustment of treatment and avoid complications, is an effective method in monitoring CVS after the treatment of intracranial aneurysm.
Keywords: Transcranial Doppler (TCD), Cerebral Vasospasm (CVS), intracranial aneurysm, craniotomy, aneurysmal clipping.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
19
2
1