摘要
尽管全球处于新冠疫情流行阶段,但目前疟疾仍是人类健康的主要威胁之一,每年世界范围内约有2亿疟疾确诊新发病例,其中约40多万死亡病例.疟疾的及时诊断对减少传播和降低死亡率都至关重要.为提高边远贫穷地区疟疾的诊断水平,以深度学习算法为基础的人工智能模型逐渐应用于显微镜检测技术以诊断血涂片中的疟原虫.本文综述了此类技术的原理,介绍了当前人工智能模型在疟疾显微镜诊断中的最新进展,并展望了深度学习和智能手机在疟疾诊断领域的应用前景.
Apart from the ongoing global pandemic of coronavirus disease 2019(COVID-19),malaria remains one of the major threats to human health.Every year,about 200 million new cases of malaria are diagnosed worldwide,resulting in more than 400,000 deaths.Timely diagnosis of malaria is critical to reducing its transmission and mortality.In order to improve the diagnosis level of malaria in remote rural areas,artificial intelligence models based on deep learning algorithms are gradually applied to the microscope for the malaria detection in blood smears.This review outlines the principles of such technologies,introduces the latest progress of current artificial intelligence models in microscopy for malaria,and looks forward to the application prospects of deep learning and smart phone technology in the field of malaria diagnosis.
作者
杨姗
李艳冰
刘拓宇
张海东
叶坤
崔玉军
张先超
滕越
YANG Shan;LI YanBing;LIU TuoYu;ZHANG HaiDong;YE Kun;CUI YuJun;ZHANG XianChao;TENG Yue(Beijing Institute of Microbiology and Epidemiology,Academy of Military Medical Sciences,Beijing 100071;State Key Laboratory of Pathogen and Biosecurity,Beijing 100071;Department of Laboratory Medicine,Xiangya Hospital,Central South University,Changsha,Hunan,410008;Institute of Automation,Chinese Academy of Sciences,Beijing 100190;Department of Laboratory Medicine,PLA General Hospital First Medical Center,Beijing,100850;Institute of Information Network and Artificial Intelligence,Jiaxing University,Jiaxing 314001)
出处
《中国科学：生命科学》
CSCD
北大核心
2022年第4期575-586,共12页
Scientia Sinica(Vitae)
关键词
疟原虫
疟疾感染
人工智能
机器学习
图像检测
malarial parasite
malaria infection
artificial intelligence
machine learning
image detection
