Beijing Tiantan Hospital, Capital Medical University

Beihang University

Nanjing Baixinyu Pharmaceutical Co. Ltd.

Yongjun Wang

Tao Liu

Jing Jing

Lingling Ding

Zixiao Li

Hao Liu

Shuo Zhang

Yanhui Li

Pan Liu

Ziyang Liu

Anyuan Zhang

Yilong Wang

Xingquan Zhao

Xuewei Xie

A method and system for extracting a multi-dimensional disconnection network region of symptom mapping: registering a lesion image to a brain standard space; obtaining diffusion magnetic resonance images and resting-state functional magnetic resonance images of healthy control groups; constructing a structural disconnection weighting network corresponding to lesions using a fiber tracking method according to the lesion image in the brain standard space and the diffusion magnetic resonance images; constructing a functional significant disconnection network corresponding to the lesions using a cross-correlation verification method according to the lesion image in the brain standard space and the resting-state functional magnetic resonance images; and determining the multi-dimensional disconnection network region of the lesions of symptom mapping according to the structural disconnection weighting network and the functional significant disconnection network, where the multi-dimensional disconnection network region of the lesions is configured to locate network mapping of a brain lesion in the brain.

Method and system for extracting multi-dimensional disconnection network region of symptom mapping

A method and system for processing an intracranial large vessel image, an electronic device, and a medium are provided. The method includes: acquiring original intracranial large vessel images of a target to be identified and a sample subject; applying a cerebrovascular segmentation model to obtain cerebrovascular mask images; computing regions of interest of the cerebrovascular mask images and bounding boxes; selecting corresponding regions of interest from the original intracranial large vessel images; separately preprocessing the mask image regions of interest and the original image regions of interest to obtain images to be processed; annotating a target region in the image to be processed of the sample subject; training a convolutional neural network model with a training set to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of the target to be identified to the cerebrovascular occlusion classification model to obtain a target region identification result.

Method and system for processing intracranial large vessel image, electronic device, and medium

A regionally integrated emergency stroke unit includes an information interaction area, an imaging examination area, and a thrombolytic therapy area. A doctor workstation, a comprehensive information surveillance monitor, and a process support artificial intelligence (AI) assistant are set in the information interaction area. A test device, a first television display, a movable low-field magnetic resonance imager, and an AI-assisted decision-making system are set in the imaging examination area. The process support AI assistant performs, via voice interaction or touchscreen keying interaction, automatic inquiry and auxiliary nervous system scale scoring, and further provides real-time retrieval support for a knowledge base and a clinical guideline. The AI-assisted decision-making system is configured to assist in decision-making based on an automatic inquiry result, a scale score result, the test result, and magnetic resonance imaging results of the head and neck, to generate and display a therapy regimen.

Regionally integrated emergency stroke unit

Disclosed in the present invention are an edaravone pharmaceutical composition and an application thereof as a sublingual preparation, the pharmaceutical composition containing edaravone or a salt thereof and mannitol. A sublingual administration preparation can avoid the first-pass effect of the liver, and samples feature good stability, convenience of transport and of use, among other advantages.

Edaravone pharmaceutical composition

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Beijing, China

Yongjun Wang