Artificial intelligence-assisted colonoscopy:A review of current state of practice and research

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs;however,colonoscopy practice can vary in terms of lesion detection,classification,and removal.Artificial intelligence(AI)-assisted decision support systems for endoscopy is an area of rapid research and development.The systems promise improved detection,classification,screening,and surveillance for colorectal polyps and cancer.Several recently developed applications for AIassisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas.However,their value for real-time application in clinical practice has yet to be determined owing to limitations in the design,validation,and testing of AI models under real-life clinical conditions.Despite these current limitations,ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination,including polypectomy procedures,are at the concept stage.However,further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice,to navigate the approval process from regulatory organizations and societies,and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety.This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Advanced imaging and artificial intelligence for Barrett's esophagus:What we should and soon will do

Barrett’s esophagus(BE)is a well-established risk factor for esophageal adenocarcinoma.It is recommended that patients have regular endoscopic surveillance,with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma.Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments,and with this aim,thorough endoscopic assessment is crucial and improves outcomes.The burden of missed neoplasia in BE is still far from being negligible,likely due to inappropriate endoscopic surveillance.Over the last two decades,advanced imaging techniques,moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies,have been introduced with the aim to enhance neoplasia detection in BE.As witnessed in other fields,artificial intelligence(AI)has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well.The aim of this commentary is to comprehensively summarize present evidence,recent research advances,and future perspectives regarding advanced imaging technology and AI in BE;the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited.It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE,in order to increase the diagnostic yield of endoscopy in clinical practice.

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35%during screening colonoscopy,especially in patients with diminutive adenomas.The use of artificial intelligence(AI)in colonoscopy has been gaining popularity by helping endoscopists in polyp detection,with the aim to increase their adenoma detection rate(ADR)and polyp detection rate(PDR)in order to reduce the incidence of interval cancers.The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos.Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR.In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival

BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Guest Editorial:Intelligent Video Surveillance and Related Technologies

Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.

A light-weight on-line action detection with hand trajectories for industrial surveillance

Most of the intelligent surveillances in the industry only care about the safety of the workers.It is meaningful if the camera can know what,where and how the worker has performed the action in real time.In this paper,we propose a light-weight and robust algorithm to meet these requirements.By only two hands'trajectories,our algorithm requires no Graphic Processing Unit(GPU)acceleration,which can be used in low-cost devices.In the training stage,in order to find potential topological structures of the training trajectories,spectral clustering with eigengap heuristic is applied to cluster trajectory points.A gradient descent based algorithm is proposed to find the topological structures,which reflects main representations for each cluster.In the fine-tuning stage,a topological optimization algorithm is proposed to fine-tune the parameters of topological structures in all training data.Finally,our method not only performs more robustly compared to some popular offline action detection methods,but also obtains better detection accuracy in an extended action sequence.

不平衡数据下基于SVM增量学习的指挥信息系统状态监控方法

针对指挥信息系统历史状态样本有限的特点,基于支持向量机(support vector machines,SVM)设计了一种面向不平衡数据的SVM增量学习方法。针对系统正常/异常状态样本不平衡的情况,首先利用支持向量生成一部分新样本,然后通过分带的思想逐带产生分布更加均匀的新样本以调节原样本集的不平衡比。针对系统监控实时性要求高且在运行过程中会有新样本不断加入的特点,采用增量学习的方式对分类模型进行持续更新,在放松KKT(Karush-Kuhn-Tucker)更新触发条件的基础上,通过定义样本重要度并引入保留率和遗忘率的方式减少了增量学习过程中所需训练的样本数量。为了验证算法的有效性和优越性,实验部分在真实系统中获得的数据集以及UCI数据集中3类6组不平衡数据集中与现有的算法进行了对比。结果表明,所提算法能够有效实现对不平衡数据的增量学习,从而满足指挥信息系统状态监控的需求。

德国联邦情报局境外通讯战略性监控及其监督的新进展

[研究目的]继2016年之后,德国联邦议会于2021年3月再次对《联邦情报局法》进行修改。该法对情报机构收集境外外国人情报采取与美国不同的立场。对德国联邦情报局境外通讯战略性监控及其监督的新进展进行研究能够把握域外大规模情报监控及其监督制约的最新动向。[研究方法]通过对比分析、规范分析,对德国联邦情报局境外通讯战略性监控及其监督机制进行系统梳理;通过理性思辨方法,对该制度的新进展进行评析。[研究结论]德国2021年修改的《联邦情报局法》以《基本法》域外效力为基础,从监控目的、具体事由、事前审批、监控所收集数据的处理与留存、特殊数据保护、监督机制等方面全面重构了境外通讯战略性监控制度,且基于《基本法》域外效力的区别立法具有正当性和合理性,但适应性测试、截取通讯量限制等制度在贯彻比例原则上仍存争议。

面向智能视频监控的空中交通管制员图像分割

为解决复杂场景下空中交通管制员检测与分割精度低、鲁棒性差的问题,提出一种基于掩码区域卷积神经网络(Mask Region-based Convolutional Neural Networks, Mask R-CNN)的管制员图像分割模型ATC Mask R-CNN(ATC Mask Region-based Convolutional Neural Networks)。首先,构建管制员监控图像数据集(ATC Monitor Image Dataset, AMID)并用于模型训练、测试;其次,在主干网络中引入瓶颈注意力模块(Bottleneck Attention Module, BAM)以增强管制员特征提取,采取改进的柔性非极大值抑制算法(Soft Non-maximum Suppression, Soft-NMS)替代NMS算法进行候选框选取,提高对遮挡目标的检测分割;最后,基于AMID进行管制员图像分割试验。结果显示:ATC Mask R-CNN的精确率、召回率和平均精度分别为96.49%、95.62%和88.84%,表明了该方法的有效性。与Mask R-CNN相比,ATC Mask R-CNN有效降低了复杂场景的不利影响,更适用于管制员工作场景,可以为管制大厅安全管理自动化应用提供技术支撑。

联合侦察监视体系数字孪生需求与构建

数字孪生可构建虚拟世界对物理世界描述、研究、评估和预测的新机制,具有广阔的军事应用前景。首先,以支撑联合侦察监视体系的设计、建设、运用和评估为目标,分析了该体系数字孪生面临的典型试验研究任务和任务特点,提出了数字孪生的能力需求和典型应用模式;然后,研究了面向联合侦察监视体系建、用、研的数字孪生构建总体思路和方法,设计了该体系数字孪生的架构;最后,建立了数字孪生的模型谱系。该项研究可为联合侦察监视体系数字化应用研究提供构建方法和技术指导。

城市智慧排水管控中多元监测体系构建研究

城市河流与排水管网污染溯源的时效性差与监测体系空间覆盖不平衡等仍是城市水环境管理的突出问题。随着无人机、物联网、水质在线监测、高精度定位等技术的发展,实现快速污染溯源的多元监测体系正逐步实现。该研究通过对中山市濠头涌区域的排水管网关键节点与河流关键断面安装分布式检测仪表,并应用无人监测船对城市河流进行地形测量、水质监测,完成了监测体系的构建和多元监测数据的获取,实现了分布式仪表与无人监测船的优势互补。此外,基于电导率与氨氮相关性系数大于0.92的结果及氨氮作为主要污染物的现状,验证了电导率作为主要水质实时监测指标的合理性。

面向国家安全风险的情报感知理论模型构建研究

[研究目的]全面掌握国家安全态势,是国家安全治理的先决条件。在情报感知的视角下,探索国家安全情报工作的新理念、新思路和新方法,推进风险监测预警能力提升。[研究方法]运用规范研究方法,首先对情报工作面临的挑战进行分析;然后对情报感知研究进展及优势展开论述;在此基础上,构建集数据感知、情报刻画、态势感知及情报响应为一体的面向国家安全风险的情报感知理论模型,并解析其内涵。[研究结论]研究表明,该模型的构建将为国家安全风险监测预警提供普适性的模型参考,为推动情报感知的实践应用奠定基础,为夯实国家安全情报能力,进而构建全域联动、立体高效的国家安全防护体系提供支撑。

基于Android平台的智能家居监控系统

目前,远程视频监控系统已成为智能家居的重要组成部分。智能监控系统基于Android操作系统平台,以平板式计算机作为客户端,通过HTTP协议从IP摄像机下载监控图片,并利用多种算法对家中各房间或不同角度的情况进行智能监控。

An executable modeling and analyzing approach to C4ISR architecture

To analyze the behavioral model of the command,control,communication,computer,intelligence,surveillance,reconnaissance(C4ISR)architecture,we propose an executable modeling and analyzing approach to it.First,the meta concept model of the C4ISR architecture is introduced.According to the meta concept model,we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML.Then,we define the concrete syntax and executable activity algebra(EAA)semantics for executable models.The semantics functions are introduced to translating the syntax description of executable models into the item of EAA.To support the execution of models,we propose the executable rules which are the structural operational semantics of EAA.Finally,an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.

基于多模态大数据的国家安全风险态势感知模型构建

[研究目的]为强化国家安全情报能力,推动风险监测预警能力提升,构建基于多模态大数据的国家安全风险态势感知模型。[研究方法]该文对国家安全风险态势感知进行阐释,并分析多模态大数据驱动下的国家安全风险态势感知运作逻辑;在此基础上,构建态势感知模型并解析其内涵。[研究结论]该模型集国家安全风险态势察觉、态势理解、态势预测及态势投射为一体,其构建可为强化国家安全风险监测预警能力提供参考,为服务国家治理体系和能力现代化建设提供支撑。

基于Android平台的智能温室视频无线监控系统

目前,我国传统农业处于向优质、高产和高效的现代化农业转化过程中,智能温室体现了现代农业发展的方向,而视频监控在智能温室中能够起到指导和监管作用。视频无线传输技术有效地解决了温室中不方便使用有线网络的问题,使我国农业现代化发展进入了一个崭新的阶段。视频监控系统基于Android操作系统平台,以Android手机作为客户端,利用WiFi技术完成视频的无线传输和指令发送,解决了智能温室中设备不能布线或很难布线时的监控问题。

“星链”军事化发展对太空情报信息安全构成的挑战与应对

[研究目的]通过分析美国“星链”计划的军事化进程,一方面探究其对美军情报传输能力、情报侦察能力以及战略情报支援能力的提升作用,另一方面就如何应对“星链”军事化导致的太空情报信息安全问题提供对策建议。[研究方法]采用文献研究法和历史研究法对“星链”军事化应用进程、相关研究成果进行梳理,评估其对全球太空情报信息安全的影响。[研究结论]美国推动“星链”军事化加剧了太空情报资源竞争,挑战太空情报资源开发利用的制度规约,强化了美国在太空信息情报领域的霸权。为应对挑战,必须加快推进卫星互联网安全防御系统建设,完善和平利用太空情报信息资源的法律规范,构建“全国一盘棋”的军地协同创新体系。

空基情报监视侦察装备智能化发展初步研究

空基情报监视侦察装备是最为典型的信息化武器装备之一,也是网络信息体系的重要支撑,因而成为军事智能化发展的重点领域。论文介绍了近年来国内外军事强国空基情报监视侦察(Intelligence,Surveillance and Reconnaissance,ISR)装备领域在探测、识别、传感器管理等领域智能化发展的主要进展,立足近期可以利用的技术成果,提出了国内空基情监侦装备智能化发展在探测感知、频谱管理与人机交互等方面的主要现实需求,并给出了强化基因理念、优化研究布局和开展示范验证等三方面的推进措施建议。