A preliminary study on establishment of AI-assisted remote imaging diagnosis system for major infectious diseases/ 中华医学科研管理杂志

中华医学科研管理杂志 Pub Date : 2020-03-03 DOI:10.3760/CMA.J.CN113565-20200214-00012

Qingyuan He, Yue Gao, Bin Cui, Hongbin Han

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引用次数: 1

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. Key words: COVID-19; AI; Image diagnosis; Collaborative diagnosis

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人工智能辅助重大传染病远程影像诊断系统建立的初步研究

目的针对新型冠状病毒肺炎等严重传染病疫情，建立基于人工智能的图像诊断系统，提高疾病诊断效率，减轻一线医生负担，改善医疗资源配置。方法利用深度卷积神经网络和区域防控辅助信息系统，对确诊患者的图像数据等信息进行综合分析处理。结果提出了一套基于人工智能的医学图像辅助数据处理系统。结合多模式医学数据协同诊断，可以有效准确地分割患者肺部CT图像中的病变区域，并生成标准化的报告。该系统依托多中心协同诊疗平台，引入多专家远程会诊机制，提高重症患者的诊断质量。结论该系统通过划分病理区域、生成标准化报告和引入多中心机制，有助于优化医疗资源配置，提高资源利用率。关键词：新冠肺炎；AI；图像诊断；协同诊断

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中华医学科研管理杂志

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