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.

Development and Missions of Unmanned Surface Vehicle

The navy and other Department of Defense organizations are increasingly interested in the use of unmanned surface vehicles (USVs) for a variety of missions and applications. The term USV refers to any vehicle that operates on the surface of the water without a crew. USVs have the potential, and in some cases the demonstrated ability, to reduce risk to manned forces, provide the necessary force multiplication to accomplish military missions, perform tasks which manned vehicles cannot, and do so in a way that is affordable for the navy. A survey of USV activities worldwide as well as the general technical challenges of USVs was presented below. A general description of USVs was provided along with their typical applications. The technical challenges of developing a USV include its intelligence level, control, high stability, and developmental cost reduction. Through the joint efforts of researchers around the world, it is believed that the development of USVs will enter a new phase in the near future, as USVs could soon be applied widely both in military and civilian service.

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。

Imaging diagnosis of bronchogenic carcinoma(the forgotten disease)during times of COVID-19 pandemic:Current and future perspectives

Patients with bronchogenic carcinoma comprise a high-risk group for coronavirus disease 2019(COVID-19),pneumonia and related complications.Symptoms of COVID-19 related pulmonary syndrome may be similar to deteriorating symptoms encountered during bronchogenic carcinoma progression.These resemblances add further complexity for imaging assessment of bronchogenic carcinoma.Similarities between clinical and imaging findings can pose a major challenge to clinicians in distinguishing COVID-19 super-infection from evolving bronchogenic carcinoma,as the above-mentioned entities require very different therapeutic approaches.However,the goal of bronchogenic carcinoma management during the pandemic is to minimize the risk of exposing patients to COVID-19,whilst still managing all life-threatening events related to bronchogenic carcinoma.The current pandemic has forced all healthcare stakeholders to prioritize per value resources and reorganize therapeutic strategies for timely management of patients with COVID-19 related pulmonary syndrome.Processing of radiographic and computed tomography images by means of artificial intelligence techniques can facilitate triage of patients.Modified and newer therapeutic strategies for patients with bronchogenic carcinoma have been adopted by oncologists around the world for providing uncompromised care within the accepted standards and new guidelines.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively,an integrated surveillance strategy needs to be applied to both human and animal health.Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat.It is not realistic to either invest only in efforts to detect emerging diseases,or to rely solely on event-based surveillance.A comprehensive strategy is needed,concurrently investigating and managing endemic zoonoses,studying evolving diseases which change their character and importance due to influences such as demographic and climatic change,and enhancing understanding of factors which are likely to influence the emergence of new pathogens.This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect.As yet there is no fully formed blueprint that can be applied in Asian countries.Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

基于android的智能家居系统中智能视频监控技术的研究

随着android系统、无线网络等新技术的发展,智能家居已悄然走入大众视野,智能监控作为配套的解决方案也被广泛推广.该文所设计的系统使用背景建模、更新等技术对目标进行检测与跟踪,排查分析可疑目标对象,通过服务器Socket通迅将信息实时的传递给客户端,提高系统的安全性.

一种基于Android的智能视频监控系统

为了实现视频监控的智能化,提出了一种基于Android 4.0操作系统的智能视频监控系统的方案,系统具有全景视频采集、图像复原和变换、异常行为检测、高清抓拍、数据回传等功能,采用基于时空特征点的Hessian矩阵检测方法进行异常行为识别,采用满全景拼接的方法实现360°全景呈现,最后设计并实现了该系统。实验结果表明,该系统稳定可靠,携带方便,功能基本实现,有一定应用前景。

CNN-Based Intelligent Safety Surveillance in Green IoT Applications

Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things(IoT)applications.However,traditional safety monitoring methods require a lot of labor source.In this paper,we propose intelligent safety surveillance(ISS)method using a convolutional neural network(CNN),which is an autosupervised method to detect workers whether or not wearing helmets.First,to train the CNN-based ISS model,the labeled datasets mainly come from two aspects:1)our labeled datasets with the full labeled on both helmet and pedestrian;2)public labeled datasets with the parts labeled either on the helmet or pedestrian.To fully take advantage of all datasets,we redesign CNN structure of network and loss functions based on YOLOv3.Then,we test our proposed ISS method based on the specific detection evaluation metrics.Finally,experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets.When the threshold of intersection over union(IoU)between the predicted box and ground truth is set to 0.5,the average precision of pedestrians and helmets can reach 0.864 and 0.891,respectively.

Intelligent Video Surveillance System for Elderly People Living Alone Based on ODVS

Intelligent video surveillance for elderly people living alone using Omni-directional Vision Sensor (ODVS) is an important application in the field of intelligent video surveillance. In this paper, an ODVS is utilized to provide a 360° panoramic image for obtaining the real-time situation for the elderly at home. Some algorithms such as motion object detection, motion object tracking, posture detection, behavior analysis are used to implement elderly monitoring. For motion detection and object tracking, a method based on MHoEI(Motion History or Energy Images) is proposed to obtain the trajectory and the minimum bounding rectangle information for the elderly. The posture of the elderly is judged by the aspect ratio of the minimum bounding rectangle. And there are the different aspect ratios in accordance with the different distance between the object and ODVS. In order to obtain activity rhythm and detect variously behavioral abnormality for the elderly, a detection method is proposed using time, space, environment, posture and action to describe, analyze and judge the various behaviors of the elderly in the paper. In addition, the relationship between the panoramic image coordinates and the ground positions is acquired by using ODVS calibration. The experiment result shows that the above algorithm can meet elderly surveillance demand and has a higher recognizable rate.

基于Android移动终端的多功能视频监控系统设计

随着嵌入式技术和移动通信技术的不断发展,利用智能移动终端设备进行远程监控已经成为网络监控研究领域的热点,本文设计了基于Android智能手机的多功能视频监控系统。该系统分为视频采集端、Android智能手机客户端、Web服务器三层设计。视频采集端利用网络摄像头采集视频,并将其以H.264标准压缩传输至服务器进行实时存储,同时利用传感器采集环境参数及有无人员入侵等信息;客户端通过TCP/IP协议与服务器建立通信,实现实时视频监控和监测数据的查阅,并对可疑情况报警与信息提示。综合测试结果表明,系统实时性高且部署灵活,获取的监控画面清晰稳定,可以满足无线网络环境下远程视频监控的要求。

System Structure and Calibration Models of Intelligent Photogrammetron

This paper describes the structure, geometric model and geometric calibrationof Photogrammetron Ⅰ - the first type of photogrammetron which is designed to be a coherent stereophotogrammetric system in which two cameras are mounted on a physical base but driven by anintelligent agent architecture. The system calibration is divided into two parts: the in-labcalibration determines the fixed parameters in advance of system operation, and the in-situcalibration keeps tracking the free parameters in real-time during the system operation. In a videosurveillance set-up, prepared control points are tracked in stereo image sequences, so that the freeparameters of the system can be continuously updated through iterative bundle adjustment and Kalmanfiltering.