Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence(AI)in gastr-ointestinal(GI)endoscopy is growing rapidly.New technologies based on machine learning(ML)and convolutional neural networks(CNNs)are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures,in detection,diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators.Platforms based on ML and CNNs require regulatory approval as medical devices.Interactions between humans and the technologies we use are complex and are influenced by design,behavioural and psychological elements.Due to the substantial differences between AI and prior technologies,important differences may be expected in how we interact with advice from AI technologies.Human-AI interaction(HAII)may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability.Human factors influencing HAII may include automation bias,alarm fatigue,algorithm aversion,learning effect and deskilling.Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

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.

Artificial intelligence in gastrointestinal endoscopy:The future is almost here

Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.

Artificial intelligence application in diagnostic gastrointestinal endoscopy-Deus ex machina?

The close relationship of medicine with technology and the particular interest in this symbiosis in recent years has led to the development of several computed artificial intelligence(AI)systems aimed at various areas of medicine.A number of studies have demonstrated that those systems allow accurate diagnoses with histological precision,thus facilitating decision-making by clinicians in real time.In the field of gastroenterology,AI has been applied in the diagnosis of pathologies of the entire digestive tract and their attached glands,and are increasingly accepted for the detection of colorectal polyps and confirming their histological classification.Studies have shown high accuracy,sensitivity,and specificity in relation to expert endoscopists,and mainly in relation to those with less experience.Other applications that are increasingly studied and with very promising results are the investigation of dysplasia in patients with Barrett's esophagus and the endoscopic and histological assessment of colon inflammation in patients with ulcerative colitis.In some cases AI is thus better than or at least equal to human abilities.However,additional studies are needed to reinforce the existing data,and mainly to determine the applicability of this technology in other indications.This review summarizes the state of the art of AI in gastroenterological pathology.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Scoping out the future:The application of artificial intelligence to gastrointestinal endoscopy

Artificial intelligence(AI)is a quickly expanding field in gastrointestinal endoscopy.Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease,a great deal of research has focused on the identification and classification of gastrointestinal malignancies.Several of the initial randomized,prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy.In addition to work focused on colorectal cancer,AI systems have also been applied to gastric,esophageal,pancreatic,and liver cancers.Despite promising results in initial studies,the generalizability of most of these AI systems have not yet been evaluated.In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

Research on the intelligent internet nursing model based on the child respiratory and asthma control test scale for asthma management of preschool children

BACKGROUND Childhood asthma is a common respiratory ailment that significantly affects preschool children.Effective asthma management in this population is particularly challenging due to limited communication skills in children and the necessity for consistent involvement of a caregiver.With the rise of digital healthcare and the need for innovative interventions,Internet-based models can potentially offer relatively more efficient and patient-tailored care,especially in children.AIM To explore the impact of an intelligent Internet care model based on the child respiratory and asthma control test(TRACK)on asthma management in preschool children.METHODS The study group comprised preschoolers,aged 5 years or younger,that visited the hospital's pediatric outpatient and emergency departments between January 2021 and January 2022.Total of 200 children were evenly and randomly divided into the observation and control groups.The control group received standard treatment in accordance with the 2016 Guidelines for Pediatric Bronchial Asthma and the Global Initiative on Asthma.In addition to above treatment,the observation group was introduced to an intelligent internet nursing model,emphasizing the TRACK scale.Key measures monitored over a six-month period included the frequency of asthma attack,emergency visits,pulmonary function parameters(FEV1,FEV1/FVC,and PEF),monthly TRACK scores,and the SF-12 quality of life assessment.Post-intervention asthma control rates were assessed at six-month follow-up.RESULTS The observation group had fewer asthma attacks and emergency room visits than the control group(P<0.05).After six months of treatment,the children in both groups had higher FEV1,FEV1/FVC,and PEF(P<0.05).Statistically significant differences were observed between the two groups(P<0.05).For six months,children in the observation group had a higher monthly TRACK score than those in the control group(P<0.05).The PCS and MCSSF-12 quality of life scores were relatively higher than those before the nursing period(P<0.05).Furthermore,the groups showed statistically significant differences(P<0.05).The asthma control rate was higher in the observation group than in the control group(P<0.05).CONCLUSION TRACK based Intelligent Internet nursing model may reduce asthma attacks and emergency visits in asthmatic children,improve lung function,quality of life,and the TRACK score and asthma control rate.The effect of nursing was significant,allowing for development of an asthma management model.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

A Study of Visual Event-Related Potential and Reaction Time in Elderly People: Comparative Analysis of the Scores of Intelligence Test in 30 Subjects

The results of visual event-related potential(ERP)examinations and reactiontime(RT)tests were reported in 30 elders and compared with their performanceintellegence quotient(PIQ)scores.The subjects consisted of 18 males and 12 femalesaged 50-71(mean 61.4)years old.No history of central nervous system disease wasfound.The visual stimuli were randomly presented to the subject,including three sym-bols:E as target stimulus with 0.15 probability,and H and E as nontarget stimuliwith 0.15 and 0.70 probability respectively.The recording electrodes were placed on Fzand Pz.The duration from the subject seeing the target to touching a button immediatelywas considered as reaction time(RT).It was shown that the P3 latency at Pz was longer than that at Fz and the P3amplitude at Pz was larger than that at Fz,and that the RT was longer than P3 latencywith obvious effect of distribution(P【0.05 at Fz and P】0.05 at Pz)as well .The higherthe PIQ scores,the longer the RT and the P3 latency.It is suggested that the ERPmight reflect the differences of PIQ scores,and the P3 is an objective index.We considerthat the research of ERP is of great interest in the neuropsychological and neurological sci-ences.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Intelligence Test

1.If you drop a white hat into the Red Sea,what does itbecome?2.What do people do in clock factories?3.Whv do seagulls live near the sea?4.A cowboy rode to an inn on Friday,stayed two nightsand 1eft on Friday.How could that be?5.Where does a bird go when it loses its tail?(Key:1.Wet.2.They make faces all day. 3.Because ifthey live near the bay they will be called bagels.4.Hishorse’s name was Friday.5.The retail store.)

English Intelligence Test

ANow,imagine(设想)there is a large live duckinside a very large bottle.

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.

Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma

BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep learning computer-assisted diagnosis(CAD)system for endoscopic detection of superficial ESCC and investigate its application value.METHODS We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm.A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets,respectively.Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance.The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve,accuracy,sensitivity,and specificity.RESULTS The area under the receiver operating characteristics curve,accuracy,sensitivity,and specificity of the CAD system were 0.982[95%confidence interval(CI):0.969-0.994],92.9%(95%CI:89.5%-95.2%),91.9%(95%CI:87.4%-94.9%),and 94.7%(95%CI:89.0%-97.6%),respectively.The accuracy of CAD was significantly higher than that of non-expert endoscopists(78.3%,P<0.001 compared with CAD)and comparable to that of expert endoscopists(91.0%,P=0.129 compared with CAD).After referring to the CAD results,the accuracy of the non-expert endoscopists significantly improved(88.2%vs 78.3%,P<0.001).Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.CONCLUSION The diagnostic performance of the CAD system is promising and may assist in improving detectability,particularly for inexperienced endoscopists.

Use of artificial intelligence in improving adenoma detection rate during colonoscopy:Might both endoscopists and pathologists be further helped

Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedurally.Computeraided detection and diagnosis(CAD),thanks to the brand new developed innovations of artificial intelligence,and especially deep-learning techniques,leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy.The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate,and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality.Furthermore,a significant reduction in costs is also expected.In addition,the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy,as it is reported in literature,addressing evidence,limitations,and future prospects.

Artificial intelligence in colonoscopy

Colorectal cancer remains a leading cause of morbidity and mortality in the United States.Advances in artificial intelligence(AI),specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps.Conversely,these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place,decreasing the risks that come with unnecessary polypectomies.This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.

Food Additives-Ascorbic Acid Quality Assay Test Intelligent Management

In this paper, based on the analysis and test methods of national standards （GB 14754-2010） and chemical analysis and test items carried out by chemical enterprises, a set of automatic processing of quality analysis test data of ascorbic acid products was developed by using access database technology and Visual Basic programming language system, and its stability was investigated. The results show that the software can manage intelligently all aspects of the quality analysis and test of ascorbic acid products, uploading timely the data and results of the analysis and inspection to the network and saving it for users, enterprises and quality management, which set up a network of information sharing platform to ensure the authenticity and reliability of measurement results, improving greatly the speed of data processing, saving valuable time, reducing production costs with good economic efficiency and social benefit. It has practical value for ascorbic acid quality analysis test data processing automatically the results of the implementation of intelligent management.

Application of artificial intelligence in hepatology:Minireview

With the rapid advancements in computer science,artificial intelligence(AI)has become an intrinsic part of our daily life and clinical practices.The concepts of AI,such as machine learning,deep learning,and big data,are extensively used in clinical and basic research.In this review,we searched for the articles in PubMed and summarized recent developments of AI concerning hepatology while focusing on the diagnosis and risk assessment of liver diseases.Ultrasound is widely conducted for the routine surveillance of hepatocellular carcinoma along with tumor markers.Computer-aided diagnosis is useful in the detection of tumors and characterization of space-occupying lesions.The prognosis of hepatocellular carcinoma can be estimated via AI using large-scale and highquality training datasets.The prevalence of nonalcoholic fatty liver disease is increasing worldwide and pivotal concern in the field is who will progress and develop hepatocellular carcinoma.Most AI studies require a large dataset,including laboratory or radiological findings and outcome data.AI will be useful in reducing medical errors,supporting clinical decisions,and predicting clinical outcomes.Thus,cooperation between AI and humans is expected to improve healthcare.

Emerging artificial intelligence applications in gastroenterology: A review of the literature

Artificial intelligence(AI)allows machines to provide disruptive value in several industries and applications.Applications of AI techniques,specifically machine learning and more recently deep learning,are arising in gastroenterology.Computer-aided diagnosis for upper gastrointestinal endoscopy has growing attention for automated and accurate identification of dysplasia in Barrett’s esophagus,as well as for the detection of early gastric cancers(GCs),therefore preventing esophageal and gastric malignancies.Besides,convoluted neural network technology can accurately assess Helicobacter pylori(H.pylori)infection during standard endoscopy without the need for biopsies,thus,reducing gastric cancer risk.AI can potentially be applied during colonoscopy to automatically discover colorectal polyps and differentiate between neoplastic and nonneoplastic ones,with the possible ability to improve adenoma detection rate,which changes broadly among endoscopists performing screening colonoscopies.In addition,AI permits to establish the feasibility of curative endoscopic resection of large colonic lesions based on the pit pattern characteristics.The aim of this review is to analyze current evidence from the literature,supporting recent technologies of AI both in upper and lower gastrointestinal diseases,including Barrett's esophagus,GC,H.pylori infection,colonic polyps and colon cancer.

Intelligent Tester for Geotextiles Products-Determination of Water Flow Capacity in their Plane

Geotextiles and geotextile-related products Determination of water flowcapacity in their plane has just National standard. But has not a formal instrument at present. There are many kinds of geotextile and also lots of factors influential to the penetration coefficient thereof. The intelligent tester may be involved in testing penetration coefficient under different pressures/gradients resulted in fine repeatability controlled intelligently by microcomputer system.