Artificial intelligence-assisted esophageal cancer management:Now and future

Esophageal cancer poses diagnostic,therapeutic and economic burdens in highrisk regions.Artificial intelligence(AI)has been developed for diagnosis and outcome prediction using various features,including clinicopathologic,radiologic,and genetic variables,which can achieve inspiring results.One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus.In this review,we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes,and combine the endoscopic images to detect precancerous lesions or early cancer.Pertinent studies conducted in recent two years have surged in numbers,with large datasets and external validation from multi-centers,and have partly achieved intriguing results of expert’s performance of AI in real time.Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets,aiming at real-time video processing,are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists.Meanwhile,supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion,which meets patient-centered satisfaction.Notably,ethical and legal issues regarding the blackbox nature of computer algorithms should be addressed,for both clinicians and regulators.

Artificial intelligence applications in inflammatory bowel disease:Emerging technologies and future directions

Inflammatory bowel disease(IBD)is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity.The rapid accumulation of large datasets from electronic health records,high-definition multi-omics(including genomics,proteomics,transcriptomics,and metagenomics),and imaging modalities(endoscopy and endomicroscopy)have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD.Although the application of artificial intelligence(AI)methods has facilitated the analysis,integration,and interpretation of large datasets in IBD,significant heterogeneity in AI methods,datasets,and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice.The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction,assessment of disease severity,and prediction of clinical outcomes in patients with IBD.

Artificial intelligence system for the detection of Barrett’s esophagus

BACKGROUND Barrett’s esophagus(BE),which has increased in prevalence worldwide,is a precursor for esophageal adenocarcinoma.Although there is a gap in the detection rates between endoscopic BE and histological BE in current research,we trained our artificial intelligence(AI)system with images of endoscopic BE and tested the system with images of histological BE.AIM To assess whether an AI system can aid in the detection of BE in our setting.METHODS Endoscopic narrow-band imaging(NBI)was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital,resulting in 724 cases,with 86 patients having pathological results.Three senior endoscopists,who were instructing physicians of the Digestive Endoscopy Society of Taiwan,independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE.The test set consisted of 160 endoscopic images of 86 cases with histological results.RESULTS Six pre-trained models were compared,and EfficientNetV2B2(accuracy[ACC]:0.8)was selected as the backbone architecture for further evaluation due to better ACC results.In the final test,the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE,resulting in an ACC of 94.37%.CONCLUSION Our AI system,which was trained by NBI of endoscopic BE,can adequately predict endoscopic images of histological BE.The ACC,sensitivity,and specificity are 94.37%,94.29%,and 94.44%,respectively.

Artificial intelligence and inflammatory bowel disease: Where are we going?

Inflammatory bowel diseases,namely ulcerative colitis and Crohn’s disease,are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide.Because of their complex and partly unknown etiology and pathogenesis,the management of ulcerative colitis and Crohn’s disease can prove challenging not only from a clinical point of view but also for resource optimization.Artificial intelligence,an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving,and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties.In this regard gastroenterology is no exception,and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well.The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis,follow-up,treatment,prognosis,cancer surveillance,data collection,and analysis.Moreover,insights into the potential further developments in this field and their effects on future clinical practice were discussed.

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 in colorectal cancer screening in patients with inflammatory bowel disease

Artificial intelligence(AI)is a branch of computer science that develops intelligent machines.In recent years,medicine has been contemplated with this recent modality to aid in the diagnosis of diseases in several specialties,including gastroenterology and gastrointestinal endoscopy.This new technology has superior ability to perform tasks mimicking human behavior and can identify possible pathological alterations,such as pre-malignant lesions and dysplasia,precursor lesions of colorectal cancer(CRC),and support medical decisionmaking.CRC is among the three most prevalent cancer types,and the second most common cause of cancer-related deaths worldwide;in addition,it is a leading cause of death in patients with inflammatory bowel disease(IBD).Patients with IBD tend to have greater inflammatory cell activity in the intestinal mucosa,which can favor cell proliferation and CRC development.AI can contribute to the detection of pre-neoplastic lesions in patients at risk of CRC development,such as those with extensive IBD or when additional CRC risk factors,such as smoking,are present.In fact,AI systems could improve all aspects of care related to both the detection of pre-malignant and malignant lesions and the screening of patients with IBD.In this review,we aimed to show the benefits and innovations of AI in the screening of CRC in patients with IBD.The promising applications of AI have the potential to revolutionize clinical practice and gastrointestinal endoscopy,especially in at-risk patients,such as those with IBD.

Artificial intelligence and early esophageal cancer

The development of esophageal cancer(EC)from early to advanced stage results in a high mortality rate and poor prognosis.Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society.Endoscopy is of great value for the diagnosis of EC,especially in the screening of Barrett’s esophagus and early EC.However,at present,endoscopy has a low diagnostic rate for early tumors.In recent years,artificial intelligence(AI)has made remarkable progress in the diagnosis of digestive system tumors,providing a new model for clinicians to diagnose and treat these tumors.In this review,we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results.We analyze and summarize the recent research on AI and early EC.We find that based on deep learning(DL)and convolutional neural network methods,the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis.Based on powerful computing and DL capabilities,the diagnostic accuracy of AI is close to or better than that of endoscopy specialists.We also analyze the shortcomings in the current AI research and corresponding improvement strategies.We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.

Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview

Urinary incontinence (UI) is a distressing condition involving involuntary loss of urine from the body. Urinary incontinence can negatively impact a person’s overall quality of life and lead them into stages of embarrassment and depression. It is an underrepresented and undertreated condition prevalent in women, especially in low socioeconomic regions where women may not be able to express their concerns due to unawareness of diagnosis and treatment/management options. There are different diagnostic and management protocols for UI;however, utilizing artificially intelligent systems is not standard care. This paper overviews the use of artificial intelligence in women’s health and as a means of cost-effectively diagnosing patients, and as an avenue for providing low-cost treatments to women that suffer from urinary incontinence in low-resource communities. Studies found that these systems, mainly utilizing artificial neural networks (ANNs) and convolutional neural networks (CNNs), served to be an effective method in diagnosing patients and providing an avenue for personalized treatment for improved patient outcomes. A simple artificial intelligence (AI) model utilizing Multilayer Perceptron (MLP) Networks was proposed to diagnose and manage urinary incontinence.

The Future of Artificial Intelligence for Alzheimer’s Disease Diagnostics

Alzheimer’s disease (AD) is a leading cause of death, yet there is no disease-modifying drug therapy currently available. It is critical to establish a diagnosis of AD before clinical system onset so that drug therapies can start earlier. Unfortunately, this is not the current standard practice. Artificial intelligence (AI) holds tremendous promise for identifying AD related structural changes in brain scan images. This paper discusses the recent applications and potential future directions for AI in AD diagnostics. Annual brain scanning and computer vision-assisted early diagnosis is encouraged, so that disease-modifying drug therapy could begin earlier in the progressive pathology.

The Mathematical and Physical Theory of Rational Human Intelligence: Complete Empirical-Digital Properties;Full Electrochemical-Mechanical Model (Part I: Mathematical Foundations)

The design of this paper is to present the first installment of a complete and final theory of rational human intelligence. The theory is mathematical in the strictest possible sense. The mathematics involved is strictly digital—not quantitative in the manner that what is usually thought of as mathematics is quantitative. It is anticipated at this time that the exclusively digital nature of rational human intelligence exhibits four flavors of digitality, apparently no more, and that each flavor will require a lengthy study in its own right. (For more information,please refer to the PDF.)

Smartphones to Account for 2/3 of World's Mobile Market by 2020,Says New GSMA Intelligence Study

11 September 2014, Hong Kong： Smartphones will account for two out of every, three mobile connections globally by 2020, according to a major new report by GSMA In- telligence, the research arm of the GSMA. The new study, ＂Smartphone forecasts and assumptions, 2007- 2020＂, finds that smartphones account for one in three mobile conneetions today, representing more than two billion mobile connections. It forecasts that the number of smartphone connections will grow three-fold over the next six years,

粗穗披碱草1H^(t)S染色体特异荧光原位杂交标记开发

小麦-粗穗披碱草1H^(t)S.1BL罗伯逊易位系高抗小麦条锈病和叶锈病,是小麦遗传改良的优异基因源。我们前期利用中国春ph1b基因突变体对该易位系进行诱导,获得了一批诱导后代材料,为了从中准确鉴定小麦-粗穗披碱草1H^(t)S小片段易位系,需要建立能够覆盖粗穗披碱草1H^(t)S染色体的荧光原位杂交(FISH)标记。本研究利用21个小麦及其近缘种探针、61个基于中国春基因组序列新开发的小麦1BS染色体探针以及40个根据简单三碱基重复序列新开发的探针对小麦-粗穗披碱草1H^(t)S.1BL易位系进行非变性FISH分析。结果显示,B74、B76和B77等36个探针(33个为新开发的)在1H^(t)S染色体上具有杂交信号,并且杂交信号可分为仅在染色体末端、仅在着丝粒处、同时在着丝粒处和近着丝粒处、覆盖1H^(t)S约3/4染色体臂、覆盖绝大部分1H^(t)S共五种类型。因此,部分探针单独使用或多个探针联合使用,其信号能覆盖1H^(t)S染色体,能够满足小麦-粗穗披碱草1H^(t)S小片段易位系鉴定,这可为小麦背景中粗穗披碱草染色质追踪提供新的检测手段。

Role of artificial intelligence in Barrett’s esophagus

The application of artificial intelligence(AI)in gastrointestinal endoscopy has gained significant traction over the last decade.One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus(BE).AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer.Apart from visual detection and diagnosis,AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides.This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.

Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease

The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity.This review discusses the expanding applications for gastric and esophageal diseases.The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention,detection,and classification of gastric cancer.The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma.Additionally,these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.

基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Research of recognizing intelligence based on commanding decision-making

In commanding decision-making, the commander usually needs to know a lot of situations（intelligence） on the adversary. Because of the military intelligence with opposability, it is inevitable that intelligence personnel take some deceptive information released by the rival as intelligence data in the process of intelligence gathering. Since the failure of intelligence is likely to lead to a serious aftereffect, the recognition of intelligence is a very important problem. An elementary research on recognizing military intelligence and puts forward a systematic processing method are made. First, the types and characteristics of military intelligence are briefly discussed, a research thought of recognizing military intelligence by means of recognizing military hypotheses are presented. Next, the reasoning mode and framework for recognizing military hypotheses are presented from the angle of psychology of intelligence analysis and non-monotonic reasoning. Then, a model for recognizing military hypothesis is built on the basis of fuzzy judgement information given by intelligence analysts. A calculative example shows that the model has the characteristics of simple calculation and good maneuverability. Last, the methods that selecting the most likely hypothesis from the survival hypotheses via final recognition are discussed.

Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations

This minireview discusses the benefits and pitfalls of machine learning,and artificial intelligence in upper gastrointestinal endoscopy for the detection and characterization of neoplasms.We have reviewed the literature for relevant publications on the topic using PubMed,IEEE,Science Direct,and Google Scholar databases.We discussed the phases of machine learning and the importance of advanced imaging techniques in upper gastrointestinal endoscopy and its association with artificial intelligence.

扩展目标跟踪Student’s t逆Wishart平滑算法

脉冲干扰和离群量测信息等因素通常会导致异常的厚尾噪声,这使得以高斯假设为前提的扩展目标跟踪(ETT)估计器的性能急剧降低,针对该问题该文提出一种基于扩展目标随机矩阵模型(RMM)的Student’s t逆Wishart平滑(StIWS)算法。首先,将目标的运动状态以及过程噪声和量测噪声建模为Student’s t分布以表征异常噪声对扩展目标概率分布的影响,将目标扩展状态建模为服从逆Wishart分布的随机矩阵。然后,在Student’s t贝叶斯平滑框架下,详细推导了能在扩展目标的多重特征动态演变的过程中有效估计目标状态的StIWS算法。最后,通过扩展目标跟踪的仿真实验结果和真实场景实验结果验证了所提算法的有效性。

车辆主动悬架自适应变论域T-S模糊控制研究

针对传统变论域模糊控制存在过度依赖专家经验、伸缩因子参数不能自适应调整的问题,提出一种车辆主动悬架自适应变论域T-S模糊控制策略,从而提高车辆的行驶平顺性。结合神经网络和T-S模糊推理建立基于自适应神经模糊推理的一阶T-S模糊控制器,利用神经网络的自学习特性产生完善的模糊规则,进而在传统函数型伸缩因子的基础上,将系统误差和误差变化率作为动态参数引入伸缩因子中,实现伸缩因子参数的自适应调整,解决了传统函数型伸缩因子因参数确定难度大导致控制效果差的问题。通过随机工况下的仿真分析和基于相似理论的缩尺实验,对所提出算法的有效性和工况自适应性进行了验证。结果表明,所提出的自适应变论域T-S模糊控制策略具有较强的工况适应性,在不同车速、路面激励下均可有效提高车辆的平顺性并保证轮胎接地安全性。