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| 1 | Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions显示文摘Artificial intelligence(AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal(GI)endoscopy.Despite vast improvements in endoscopic techniques and equipment,diagnostic endoscopy remains heavily operator-dependent,in particular,colonoscopy and endoscopic ultrasound(EUS).Recent reports have shown that as much as 25%of colonic adenomas may be missed at colonoscopy.This can result in an increased incidence of interval colon cancer.Similarly,EUS has been shown to have high inter-observer variability,overlap in diagnoses with a relatively low specificity for pancreatic lesions.Our understanding of Machine-learning(ML)techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers.ML is an aspect of AI that is based on neural networks,and is widely used for image classification,object detection,and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems.In this review,current status and limitations of ML,specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions,will be summarized from existing literature.This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy. | Harshavardhan Rao B Judy A Trieu Priya Nair Gilad Gressel Mukund Venu Rama P Venu | 2022 | Artificial Intelligence in Gastrointestinal Endoscopy2022，3，3： | 1 |
| 2 | Colonoscopy and artificial intelligence:Bridging the gap or a gap needing to be bridged?显示文摘Research in artificial intelligence(AI)in gastroenterology has increased over the last decade.Colonoscopy represents the most widely published field with regards to its use in gastroenterology.Most studies to date center on polyp detection and characterization,as well as real-time evaluation of adequacy of mucosal exposure for inspection.This review article discusses how advances in AI has bridged certain gaps in colonoscopy.In addition,the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored. | James Weiquan Li Tiing Leong Ang | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，2： | 1 |
| 3 | Artificial intelligence in polyp detection-where are we and where are we headed?显示文摘The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer.Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks.Computer aided detection(CADe)utilizing neural networks allows real time detection of polyps and adenomas.Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies.We review the most recent prospective randomized controlled trials here.These randomized control trials,both non-blinded and blinded,demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes.Increase of polyps and adenomas detected were mainly small and sessile in nature.CADe systems were found to be safe with little added time to the overall procedure.Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy.Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions,non-blinded arms,and question of external validity. | Kristen E Dougherty Vatche J Melkonian Grace A Montenegro | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，6： | 0 |
| 4 | Artificial intelligence and machine learning in colorectal cancer显示文摘Colorectal cancer(CRC)is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US.Artificial intelligence(AI)with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types.This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them.Furthermore,we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions.Considering the evolving transition to more personalized and tailored treatment strategies for CRC,the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes.We also discuss the constraints and limitations of the use of AI systems.While the medical profession remains enthusiastic about the future of AI and machine learning,large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used. | Muhammad Awidi Arindam Bagga | 2022 | Artificial Intelligence in Gastrointestinal Endoscopy2022，3，3： | 0 |
| 5 | Artificial intelligence in Barrett’s esophagus: A renaissance but not a reformation显示文摘Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States.The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus(BE).Currently,identification of early dysplasia in BE patients requires an experienced endoscopist performing a diagnostic endoscopy with random 4-quadrant biopsies taken every 1-2 cm using appropriate surveillance intervals.Currently,there is significant difficulty for endoscopists to distinguish different forms of dysplastic BE as well as early adenocarcinoma due to subtleties in mucosal texture and color.This obstacle makes taking multiple random biopsies necessary for appropriate surveillance and diagnosis.Recent advances in artificial intelligence(AI)can assist gastroenterologists in identifying areas of likely dysplasia within identified BE and perform targeted biopsies,thus decreasing procedure time,sedation time,and risk to the patient along with maximizing potential biopsy yield.Though using AI represents an exciting frontier in endoscopic medicine,recent studies are limited by selection bias,generalizability,and lack of robustness for universal use.Before AI can be reliably employed for BE in the future,these issues need to be fully addressed and tested in prospective,randomized trials.Only after that is achieved,will the benefit of AI in those with BE be fully realized. | Karen Chang Christian S Jackson Kenneth J Vega | 2020 | Artificial Intelligence in Gastrointestinal Endoscopy2020，1，2： | 0 |
| 6 | Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?显示文摘Wireless capsule endoscopy(WCE)enables physicians to examine the gastrointestinal tract by transmitting images wirelessly from a disposable capsule to a data recorder.Although WCE is the least invasive endoscopy technique for diagnosing gastrointestinal disorders,interpreting a WCE study requires significant time effort and training.Analysis of images by artificial intelligence,through advances such as machine or deep learning,has been increasingly applied to medical imaging.There has been substantial interest in using deep learning to detect various gastrointestinal disorders based on WCE images.This article discusses basic knowledge of deep learning,applications of deep learning in WCE,and the implementation of deep learning model in a clinical setting.We anticipate continued research investigating the use of deep learning in interpreting WCE studies to generate predictive algorithms and aid in the diagnosis of gastrointestinal disorders. | Amporn Atsawarungruangkit Yousef Elfanagely Akwi W Asombang Abbas Rupawala Harlan G Rich | 2020 | Artificial Intelligence in Gastrointestinal Endoscopy2020，1，2： | 0 |
| 7 | Kyoto classification of gastritis,virtual chromoendoscopy and artificial intelligence:Where are we going?What do we need?显示文摘Chronic gastritis(CG)is a widespread and frequent disease,mainly caused by Helicobacter pylori infection,which is associated with an increased risk of gastric cancer.Virtual chromoendoscopy improves the endoscopic diagnostic efficacy,which is essential to establish the most appropriate therapy and to enable cancer prevention.Artificial intelligence provides algorithms for the diagnosis of gastritis and,in particular,early gastric cancer,but it is not yet used in practice.Thus,technological innovation,through image resolution and processing,optimizes the diagnosis and management of CG and gastric cancer.The endoscopic Kyoto classification of gastritis improves the diagnosis and management of this disease,but through the analysis of the most recent literature,new algorithms can be proposed. | Alba Panarese Yutaka Saito Rocco Maurizio Zagari | 2023 | Artificial Intelligence in Gastrointestinal Endoscopy2023，4，1： | 0 |
| 8 | Artificial intelligence in the endoscopic approach of biliary tract diseases:A current review显示文摘In recent years there have been major developments in the field of artificial intelligence.The different areas of medicine have taken advantage of this tool to make various diagnostic and therapeutic methods more effective,safe,and userfriendly.In this way,artificial intelligence has been an increasingly present reality in medicine.In the field of Gastroenterology,the main application has been in the detection and characterization of colonic polyps,but an increasing number of studies have been published on the application of deep learning systems in other pathologies of the gastrointestinal tract.Evidence of the application of artificial intelligence in the assessment of biliary tract is still scarce.Some studies support the usefulness of these systems in the investigation and treatment of choledocholithiasis,demonstrating that they have the potential to be integrated into clinical practice and endoscopic procedures,such as endoscopic retrograde cholangiopancreatography.Its application in cholangioscopy for the investigation of undetermined biliary strictures also seems to be promising.Assessing the bile duct through endoscopic ultrasound can be challenging,especially for less experienced operators,thus becoming an area of potential interest for artificial intelligence.In this review,we summarize the state of the art of artificial intelligence in the endoscopic diagnosis and treatment of biliary diseases. | Fábio Pereira Correia Luís Carvalho Lourenço | 2022 | Artificial Intelligence in Gastrointestinal Endoscopy2022，3，2： | 0 |
| 9 | Role of endoscopic ultrasound in non-variceal upper gastrointestinal bleeding management显示文摘Non-variceal upper gastrointestinal bleeding(NVUGIB)is one of the challenging situations in clinical practice.Despite that gastric ulcer and duodenal ulcer are still the main causes of acute NVUGIB,there are other causes of bleeding which might not always be detected through the standard endoscopic evaluation.Standard endoscopic management of UGIB consists of injection,thermal coagulation,hemoclips,and combination therapy.However,these methods are not always successful for rebleeding prevention.Endoscopic ultrasound(EUS)has been used recently for portal hypertension management,especially in managing acute variceal bleeding.EUS has been considered a better tool to visualize the bleeding vessel in gastroesophageal variceal bleeding.There have been studies looking at the role of EUS for managing NVUGIB;however,most of them are case reports.Therefore,it is important to review back to see the evolution and innovation of endoscopic treatment for NVUGIB and the role of EUS for possibility to replace the standard endoscopic haemostasis management in daily practice. | Cosmas Rinaldi Adithya Lesmana | 2023 | Artificial Intelligence in Gastrointestinal Endoscopy2023，4，2： | 0 |
| 10 | Artificial intelligence fails to improve colonoscopy quality:A single centre retrospective cohort study显示文摘BACKGROUND Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy(AIAC)outside of clinical trials.AIM To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy(CC).METHODS This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane,Australia.CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023.Endoscopists who conducted over 50 procedures before and after AIAC introduction were included.Procedures for surveillance of inflammatory bowel disease were excluded.Patient demographics,proceduralist specialisation,indication for colonoscopy,and colonoscopy quality metrics were collected.Adenoma detection rate(ADR)and sessile serrated lesion detection rate(SSLDR)were calculated for both AIAC and CC.RESULTS The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists.Baseline patient demographics were similar,with median age of 60 years with a slight female predominance(52.1%).Procedure indications,bowel preparation quality,and caecal intubation rates were comparable between groups.AIAC had a slightly longer withdrawal time compared to CC,but the difference was not statistically significant.The introduction of AIAC did not significantly change ADR(52.1%for AIAC vs 52.6%for CC,P=0.91)or SSLDR(17.4%for AIAC vs 18.1%for CC,P=0.44).CONCLUSION The implementation of AIAC failed to improve key markers of colonoscopy quality,including ADR,SSLDR and withdrawal time.Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists. | Naeman Goetz Katherine Hanigan Richard Kai-Yuan Cheng | 2023 | Artificial Intelligence in Gastrointestinal Endoscopy2023，4，2： | 0 |
| 11 | Artificial intelligence and early esophageal cancer显示文摘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. | Ning Li Shi-Zhu Jin | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，5： | 0 |
| 12 | Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection显示文摘With the appearance and prevalence of deep learning,artificial intelligence(AI)has been broadly studied and made great progress in various fields of medicine,including gastroenterology.Helicobacter pylori(H.pylori),closely associated with various digestive and extradigestive diseases,has a high infection rate worldwide.Endoscopic surveillance can evaluate H.pylori infection situations and predict the risk of gastric cancer,but there is no objective diagnostic criteria to eliminate the differences between operators.The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H.pylori infection,which is superior to novice endoscopists and similar to skilled.Compared with the visual diagnosis of H.pylori infection by endoscopists,AI possesses voluminous advantages:High accuracy,high efficiency,high quality control,high objectivity,and high-effect teaching.This review summarizes the previous and recent studies on AI-assisted diagnosis of H.pylori infection,points out the limitations,and puts forward prospect for future research. | Yi-Fan Lu Bin Lyu | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，3： | 0 |
| 13 | Progress and prospects of artificial intelligence in colonoscopy显示文摘Artificial intelligence(AI)is a branch of computer science.As a new technological science,it mainly develops and expands human intelligence through the research of intelligence theory,methods and technology.In the medical field,AI has bright application prospects(for example:imaging,diagnosis and treatment).The exploration of robotic gastroscopy and colonoscopy systems is not only a bold attempt,but also an inevitable trend of AI in the development of digestive endoscopy in the future.Based on the current research findings,this article summarizes the research progress of colonoscopy,and looking forward for the application of AI in colonoscopy. | Rui-Gang Wang | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，3： | 0 |
| 14 | Application of convolutional neural network in detecting and classifying gastric cancer显示文摘Gastric cancer(GC)is the fifth most common cancer in the world,and at present,esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC.Convolutional neural networks(CNNs)are a type of deep learning model and have been widely used for image analysis.This paper reviews the application and prospects of CNNs in detecting and classifying GC,aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies. | Xin-Yi Feng Xi Xu Yun Zhang Ye-Min Xu Qiang She Bin Deng | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，3： | 0 |
| 15 | Utility of artificial intelligence in colonoscopy显示文摘Colorectal cancer is one of the major causes of death worldwide.Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer.However,the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled.It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps.Introduction of artificial intelligence(AI)in the field of medicine has become popular among physicians nowadays.The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies.Moreover,AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy,assessing disease severity and predicting clinical outcomes.We conducted a literature review on the available evidence on use of AI in colonoscopy.In this review article,we discuss about the principles,application,limitations,and future aspects of AI in colonoscopy. | Niel Shah Abhilasha Jyala Harish Patel Jasbir Makker | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，3： | 0 |
| 16 | Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies显示文摘The application of artificial intelligence(AI)using deep learning and machine learning approaches in modern medicine is rapidly expanding.Within the field of Gastroenterology,AI is being evaluated across a breadth of clinical and diagnostic applications including identification of pathology,differentiation of disease processes,and even automated procedure report generation.Many pancreatic pathologies can have overlapping features creating a diagnostic dilemma that provides a window for AI-assisted improvement in current evaluation and diagnosis,particularly using endoscopic ultrasound.This topic highlight will review the basics of AI,history of AI in gastrointestinal endoscopy,and prospects for AI in the evaluation of autoimmune pancreatitis,pancreatic ductal adenocarcinoma,chronic pancreatitis and intraductal papillary mucinous neoplasm. | Ravinder Mankoo Ahmad H Ali Ghassan M Hammoud | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，3： | 0 |
| 17 | Artificial intelligence:Applications in critical care gastroenterology显示文摘Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions.However,these patients form a vulnerable group,who are at risk for developing side effects and complications.Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients.Artificial intelligence(AI)is a rapidly evolving technology with several potential applications in healthcare settings.ICUs produce a large amount of data,which may be employed for creation of AI algorithms,and provide a lucrative opportunity for application of AI.However,the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care. | Deven Juneja | 2024 | Artificial Intelligence in Gastrointestinal Endoscopy2024，5，1： | 0 |
| 18 | Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems显示文摘BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP. | Quirine Eunice Wennie van der Zander Ramon M Schreuder Ayla Thijssen Carolus H J Kusters Nikoo Dehghani Thom Scheeve Bjorn Winkens Mirjam C M van der Ende-van Loon Peter H N de With Fons van der Sommen Ad A M Masclee Erik J Schoon | 2024 | Artificial Intelligence in Gastrointestinal Endoscopy2024，5，1： | 0 |
| 19 | Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease显示文摘Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease(IBD)[comprises Crohn's disease(CD)and ulcerative colitis(UC)].Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments.Yet,disharmony is found among experts;even when reassessed by the same expert.Some studies have demonstrated that the evaluation is no better than flipping a coin.In UC,the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77(or 77%agreement adjustment for chance/accident).This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care.Lately,across all medical specialities,computer assistance has become increasingly interesting.Especially after the emanation of machine learning–colloquially referred to as artificial intelligence(AI).Compared to other data analysis methods,the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs.It is therefore evident that with such a model,one hopes to be able to remove inconsistency among humans and standardise the results across educational levels,nationalities and resources.This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC.However,due to its recent place in IBD,there is a great inconsistency between the results,as well as the reporting of the same.In this opinion review,we will explore and evaluate the method and results of the published studies utilising AI within IBD(with examples),and discuss the future possibilities AI can offer within IBD. | Bobby Lo Johan Burisch | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，4： | 0 |
| 20 | Robotic pancreaticoduodenectomy:Where do we stand?显示文摘Pancreaticoduodenectomy(PD)is a complex operation accompanied by significant morbidity rates.Due to this complexity,the transition to minimally invasive PD has lagged behind other abdominal surgical operations.The safety,feasibility,favorable post-operative outcomes of robotic PD have been suggested by multiple studies.Compared to open surgery and other minimally invasive techniques such as laparoscopy,robotic PD offers satisfactory outcomes,with a non-inferior risk of adverse events.Trends of robotic PD have been on rise with centers substantially increasing the number the operation performed.Although promising,findings on robotic PD need to be corroborated in prospective trials. | Hussein H Khachfe Joseph R Habib Mohamad A Chahrour Ibrahim Nassour | 2021 | Artificial Intelligence in Gastrointestinal Endoscopy2021，2，4： | 0 |
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