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1.
Статья в Китайский | WPRIM | ID: wpr-1020327

Реферат

The purpose of this study was to review the research status of teatment position in patients with severe craniocerebral injury. It was intended to introduce the relevant treatment position, the relationship between the position and intracranial pressure, cerebral perfusion pressure, and the application status of treatment position, it provided evidence for the rehabilitation and nursing of patients with severe craniocerebral injury.

2.
Chinese Journal of Neuromedicine ; (12): 537-540, 2018.
Статья в Китайский | WPRIM | ID: wpr-1034816

Реферат

Postural treatment for acute cerebral infarction involves supine position,lateral position and head elevation.In recent decades,controversy arises concerning determination between supine position and head elevation.Head elevation may decrease intracranial pressure and reduce incidence of pneumonia while supine position may strengthen cerebral perfusion and improve oxygenation indexes in the infarction area to promote recanalization.There has been no clear clinical evidence to determine the best treatment or rehabilitation position for patients with acute cerebral infarction.This article,hoping to provide references for clinical choice of treatment position,reviews the concept ofpostural treatment for patients with acute anterior circulation infarction,impacts of position on cerebral blood flow,arterial oxygen saturation,intracranial pressure and incidence of pneumonia,and possible ways of the impacts as well.

3.
Статья в Китайский | WPRIM | ID: wpr-774501

Реферат

Treatment position recognition in medical images is a key technique in medical image processing. Due to the excellent performance of convolutional neural networks on features extraction and classification, an architecture of parallel convolutional neural networks is proposed to recognize treatment positions in X-ray images, which uses convolution kernels of different sizes to extract local features of different sizes in these images. The experimental analysis shows that parallel convolution neural networks, which can extract representative image features with more dimensions, are competent to classify and recognize treatment positions in medical images.


Тема - темы
Algorithms , Image Processing, Computer-Assisted , Neural Networks, Computer , X-Rays
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