Standard-definition White-light,High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas:A Multicenter Randomized Controlled Trial

Objective This study aimed to compare the performance of standard-definition white-light endoscopy(SD-WL),high-definition white-light endoscopy(HD-WL),and high-definition narrow-band imaging(HD-NBI)in detecting colorectal lesions in the Chinese population.Methods This was a multicenter,single-blind,randomized,controlled trial with a non-inferiority design.Patients undergoing endoscopy for physical examination,screening,and surveillance were enrolled from July 2017 to December 2020.The primary outcome measure was the adenoma detection rate(ADR),defined as the proportion of patients with at least one adenoma detected.The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression.Results Out of 653 eligible patients enrolled,data from 596 patients were analyzed.The ADRs were 34.5%in the SD-WL group,33.5%in the HD-WL group,and 37.5%in the HD-NBI group(P=0.72).The advanced neoplasm detection rates(ANDRs)in the three arms were 17.1%,15.5%,and 10.4%(P=0.17).No significant differences were found between the SD group and HD group regarding ADR or ANDR(ADR:34.5%vs.35.6%,P=0.79;ANDR:17.1%vs.13.0%,P=0.16,respectively).Similar results were observed between the HD-WL group and HD-NBI group(ADR:33.5%vs.37.7%,P=0.45;ANDR:15.5%vs.10.4%,P=0.18,respectively).In the univariate and multivariate logistic regression analyses,neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL(HD-WL:OR 0.91,P=0.69;HD-NBI:OR 1.15,P=0.80).Conclusion HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients.It can be concluded that HD-NBI or HD-WL is not superior to SD-WL,but more effective instruction may be needed to guide the selection of different endoscopic methods in the future.Our study’s conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources,especially advanced imaging technologies.

Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022

BACKGROUND Recently,artificial intelligence(AI)has been widely used in gastrointestinal endoscopy examinations.AIM To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.METHODS Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms“AI”and“endoscopy”.The following information was recorded from the included publications:Title,author,institution,country,endoscopy type,disease type,performance of AI,publication,citation,journal and H-index.RESULTS A total of 446 studies were included.The number of articles reached its peak in 2021,and the annual citation numbers increased after 2006.China,the United States and Japan were dominant countries in this field,accounting for 28.7%,16.8%,and 15.7%of publications,respectively.The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution.“Cancer”and“polyps”were the hotspots in this field.Colorectal polyps were the most concerning and researched disease,followed by gastric cancer and gastrointestinal bleeding.Conventional endoscopy was the most common type of examination.The accuracy of AI in detecting Barrett’s esophagus,colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%,93.7%and 88.3%,respectively.The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3%and 96.2%,respectively.CONCLUSION AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.

Evaluation of Fujinon intelligent chromo endoscopy-assisted capsule endoscopy in patients with obscure gastroenterology bleeding

AIM:To investigate the potential benef it of Fujinon in-telligent chromo endoscopy(FICE)-assisted small bowel capsule endoscopy(SBCE)for detection and character-ization of small bowel lesions in patients with obscure gastroenterology bleeding(OGIB).METHODS:The SBCE examinations(Pillcam SB2,Giv-en Imaging Ltd)were retrospectively analyzed by two GI fellows(observers)with and without FICE enhance-ment.Randomization was such that a fellow did not assess the same examination with and without FICE enhancement.The senior consultant described f indings as P0,P1 and P2 lesions(non-pathological,intermedi-ate bleed potential,high bleed potential),which were considered as reference f indings.Main outcome mea-surements:Inter-observer correlation was calculated using kappa statistics.Sensitivity and specif icity for P2 lesions was calculated for FICE and white light SBCE.RESULTS:In 60 patients,the intra-class kappa cor-relations between the observers and reference f indings were 0.88 and 0.92(P2),0.61 and 0.79(P1),for SBCE using FICE and white light,respectively.Overall 157 le-sions were diagnosed using FICE as compared to 114 with white light SBCE(P = 0.15).For P2 lesions,the sensitivity was 94% vs 97% and specif icity was 95% vs 96% for FICE and white light,respectively.Five(P2 le-sions)out of 55 arterio-venous malformations could be better characterized by FICE as compared to white light SBCE.Significantly more P0 lesions were diagnosed when FICE was used as compared to white light(39 vs 8,P < 0.001).CONCLUSION:FICE was not better than white light for diagnosing and characterizing signif icant lesions on SBCE for OGIB.FICE detected signif icantly more non-pathological lesions.Nevertheless,some vascular le-sions could be more accurately characterized with FICE as compared to white light SBCE.

Gastric and duodenal polyps in familial adenomatous polyposis patients: Conventional endoscopy vs virtual chromoendoscopy(fujinon intelligent color enhancement) in dysplasia evaluation

AIM To test the fujinon intelligent color enhancement(FICE) in identifying dysplastic or adenomatous polyps in familial adenomatous polyposis(FAP) patients.METHODS Seventy-six consecutive FAP patients, already treated by colectomy and members of sixty-five families, were enrolled. A FICE system for the upper gastro-intestinal tract with an electronic endoscope system and a standard duodenoscope(for side-viewing examination) were used by two expert examiners. Endoscopic resection was performed with diathermic loop for polyps ≥ 6 mm and with forceps for polyps < 6 mm. Formalin-fixed biopsy specimens were analyzed by two expert gastrointestinal pathologists blinded to size, location and number of FAPassociated fundic gland polyps.RESULTS Sixty-nine(90.8%) patients had gastric polyps(34 only in the corpus-fundus, 7 only in the antrum and 28 in the whole stomach) and 52(68.4%) in duodenum(7 in the bulb, 35 in second/third duodenal portion, 10 both in the bulb and the second portion of duodenum). In the stomach fundus after FICE evaluation, 10 more polyps were removed from 10 patients for suspicious features of dysplasia or adenomas, but they were classified as cystic fundic gland after histology. In the antrum FICE identified more polyps than traditional endoscopy, showing a better tendency to identify adenomas and displastic areas. In the duodenum FICE added a significant advantage in identifying adenomas in the bulb and identified more polyps in the Ⅱ/Ⅲ portion.CONCLUSION FICE significantly increases adenoma detection rate in FAP patients but does not change any Spigelman stage and thus does not modify patient's prognosis and treatment strategies.

Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis:A systematic review and meta-analysis

BACKGROUND Subepithelial lesions(SELs)are gastrointestinal tumors with heterogeneous malignant potential.Endoscopic ultrasonography(EUS)is the leading method for evaluation,but without histopathological analysis,precise differentiation of SEL risk is limited.Artificial intelligence(AI)is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.AIM To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs,especially lesions originating from the muscularis propria layer.METHODS Electronic databases including PubMed,EMBASE,and Cochrane Library were searched.Patients of any sex and>18 years,with SELs assessed by EUS AIassisted,with previous histopathological diagnosis,and presented sufficient data values which were extracted to construct a 2×2 table.The reference standard was histopathology.The primary outcome was the accuracy of AI for gastrointestinal stromal tumor(GIST).Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma(GIL),the diagnostic performance of experienced endoscopists for GIST,and GIST vs GIL.Pooled sensitivity,specificity,positive,and negative predictive values were calculated.The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.RESULTS Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis.The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92%[95%confidence interval(CI):0.89-0.95;P<0.01),specificity of 80%(95%CI:0.75-0.85;P<0.01),and area under the curve(AUC)of 0.949.For diagnosis of GIST vs GIL by AI-assisted EUS,specificity was 90%(95%CI:0.88-0.95;P=0.02)and AUC of 0.966.The experienced endoscopists’values were sensitivity of 72%(95%CI:0.67-0.76;P<0.01),specificity of 70%(95%CI:0.64-0.76;P<0.01),and AUC of 0.777 for GIST.Evaluating GIST vs GIL,the experts achieved a sensitivity of 73%(95%CI:0.65-0.80;P<0.01)and an AUC of 0.819.CONCLUSION AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs,especially for GIST,demonstrating superiority compared to experienced endoscopists’and improving their diagnostic performance in the absence of invasive procedures.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Artificial intelligence in detection of small bowel lesions and their bleeding risk:A new step forward

The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate.The development of artificial intelligence(AI)in CE could simplify physicians’tasks.The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk,and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

Kyoto classification of gastritis,virtual chromoendoscopy and artificial intelligence:Where are we going?What do we need?

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.

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.

Evolving role of artificial intelligence in gastrointestinal endoscopy

Artificial intelligence(AI)is a combination of different technologies that enable machines to sense,comprehend,and learn with human-like levels of intelligence.AI technology will eventually enhance human capability,provide machines genuine autonomy,and reduce errors,and increase productivity and efficiency.AI seems promising,and the field is full of invention,novel applications;however,the limitation of machine learning suggests a cautious optimism as the right strategy.AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care.AI using deep learning technology has been used to identify,differentiate catalog images in several medical fields including gastrointestinal endoscopy.The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems.AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation.These systems can make gastroenterology practice easier,faster,more reliable,and reduce inter-observer variability in the coming years.However,the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future.In this review,we discuss AI and associated various technological terminologies,evolving role in gastrointestinal endoscopy,and future possibilities.

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available.The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions.Simultaneously,with the development of convolutional neural network,artificial intelligence(AI)has made unprecedented breakthroughs in medical imaging,including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding.In the past demi-decade,applications of AI systems in gastric cancer have also emerged.With AI’s efficient computational power and learning capacities,endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes.So far,several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes,with most systems achieving an accuracy of more than 80%.However,their feasibility,effectiveness,and safety in clinical practice remain to be seen as there have been no clinical trials yet.Nonetheless,AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection,treatment guidance and prognosis prediction of gastric lesions.This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.

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.

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-assisted endoscopic detection of esophageal neoplasia in early stage:The next step?

Esophageal cancer(EC)is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa.It has been confirmed that early EC lesions can be cured by endoscopic therapy,and the curative effect is equivalent to that of surgical operation.Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis.The accuracy of endoscopic examination results largely depends on the professional level of the examiner.Artificial intelligence(AI)has been applied in the screening of early EC and has shown advantages;notably,it is more accurate than less-experienced endoscopists.This paper reviews the application of AI in the field of endoscopic detection of early EC,including squamous cell carcinoma and adenocarcinoma,and describes the relevant progress.Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images,AIassisted real-time detection based on live-stream video may be the next step.

Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence

Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset.Often metastatic or locally invasive when symptomatic,most patients are deemed inoperable.In those who are symptomatic,multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma.In asymptomatic patients,detected pancreatic lesions can be either solid or cystic.The clinical implications of identifying small asymptomatic solid pancreatic lesions(SPLs)of<2 cm are tantamount to a better outcome.The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early,driving the development of existing imaging tools while promoting more comprehensive screening programs.An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound(EUS).It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy(FNA/FNB).Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography,both having improved the specificity of FNA and FNB.This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging,artificial intelligence.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy

BACKGROUND Small intestinal vascular malformations(angiodysplasias)are common causes of small intestinal bleeding.While capsule endoscopy has become the primary diagnostic method for angiodysplasia,manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload,which affects the accuracy of diagnosis.AIM To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine,achieve automatic disease detection,and shorten the capsule endoscopy(CE)reading time.METHODS A convolutional neural network semantic segmentation model with a feature fusion method,which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour,thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions,was proposed.Resnet-50 was used as the skeleton network to design the fusion mechanism,fuse the shallow and depth features,and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia.The training set and test set were constructed and compared with PSPNet,Deeplab3+,and UperNet.RESULTS The test set constructed in the study achieved satisfactory results,where pixel accuracy was 99%,mean intersection over union was 0.69,negative predictive value was 98.74%,and positive predictive value was 94.27%.The model parameter was 46.38 M,the float calculation was 467.2 G,and the time length to segment and recognize a picture was 0.6 s.CONCLUSION Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.

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 for endoscopy

In recent times,there has been progressive development in artificial intelligence(AI)following the introduction of deep learning in the medical field including gastroenterology and endoscopy.Most of the reported studies were based on retrospective data.Several prospective studies of real-time diagnosis of moving images using the AI system are expected to match the real clinical situation and to aid the endoscopists in the detection and diagnosis of neoplasms without missing any lesion.AI can read a large number of endoscopic images in a few minutes and make a diagnosis;therefore,it is expected to cover the lack of support for the screening esophagogastroduodenoscopy in the health check-up and a large number of capsule images,thereby freeing the endoscopists from this burden.AI can help make the diagnosis during the endoscopic procedure and thereby prevent an unnecessary biopsy for patients taking antithrombotic drugs.AI can also be useful for education and training in endoscopy.Trainees can learn to perform endoscopy and the detection and diagnosis of lesions by the support of AI.In the near future,real-time endoscopic diagnosis using AI is expected to lessen the burden of endoscopists,to enhance the quality level of endoscopists,to overcome the miss of lesions and to make optimal diagnosis.

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.