A preliminary study on establishment of AI-assisted remote imaging diagnosis system for major infectious diseases
10.3760/cma.j.cn113565-20200214-00012
- VernacularTitle: 重大传染病AI辅助下远程影像诊断体系的建立初探
- Author:
Qingyuan HE
1
;
Yue GAO
2
;
Bin CUI
3
;
Hongbin HAN
4
Author Information
1. Radiology Department, Peking University Third Hospital, Beijing 100191, China; Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Beijing 100191, China
2. School of Software, Tsinghua University, Beijing 100084, China
3. Radiology Department, Aerospace Center Hospital, Beijing 100049, China
4. Radiology Department, Peking University Third Hospital, Beijing 100191, China; Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Beijing 100191, China; Institute of Medical Technology, Peking University Health Science Center, Beijing 100049, China
- Publication Type:Journal Article
- Keywords:
COVID-19;
AI;
Image diagnosis;
Collaborative diagnosis
- From:
Chinese Journal of Medical Science Research Management
2020;33(0):E010-E010
- CountryChina
- Language:Chinese
-
Abstract:
Objective:In response to the outbreak of severe infectious diseases such as new coronavirus pneumonia, an artificial intelligence based image diagnosis system is established to improve the efficiency of disease diagnosis, reduce the burden of front-line doctors, and improve the medical resource allocation.
Methods:Using the deep convolution neural network and regional prevention and control auxiliary information system, the image data and other information of the confirmed patients were analyzed and processed comprehensively.
Results:A set of AI based medical image auxiliary data processing system is proposed. Combined with multimodal medical data collaborative diagnosis, it can effectively and accurately segment the diseased areas in patients' lung CT images, and generate standardized reports. Relying on the multi-center collaborative diagnosis and treatment platform, the system can introduce multi-expert remote consultation mechanism to improve the diagnosis quality of severe patients.
Conclusions:By segmenting pathological regions, generating standardized reports and introducing multicenter mechanism, the system can help to optimize the medical resources allocation and improve the utilization of these resources.
