Clinical usefulness of linked color imaging in identifying Helicobacter pylori infection:A systematic review and meta-analysis

BACKGROUND Accurate diagnosis of Helicobacter pylori(H.pylori)infection status is a crucial premise for eradication therapy,as well as evaluation of risk for gastric cancer.Recent progress on imaging enhancement endoscopy(IEE)made it possible to not only detect precancerous lesions and early gastrointestinal cancers but also to predict H.pylori infection in real time.As a novel IEE modality,linked color imaging(LCI)has exhibited its value on diagnosis of lesions of gastric mucosa through emphasizing minor differences of color tone.AIM To compare the efficacy of LCI for H.pylori active infection vs conventional white light imaging(WLI).METHODS PubMed,Embase,Embase and Cochrane Library were searched up to the end of April 11,2022.The random-effects model was adopted to calculate the diagnostic efficacy of LCI and WLI.The calculation of sensitivity,specificity,and likelihood ratios were performed;symmetric receiver operator characteristic(SROC)curves and the areas under the SROC curves were computed.Quality of the included studies was chosen to assess using the quality assessment of diagnostic accuracy studies-2 tool.RESULTS Seven original studies were included in this study.The pooled sensitivity,specificity,positive likelihood rate,and negative likelihood rate of LCI for the diagnosis of H.pylori infection of gastric mucosa were 0.85[95%confidence interval(CI):0.76-0.92],0.82(95%CI:0.78-0.85),4.71(95%CI:3.7-5.9),and 0.18(95%CI:0.10-0.31)respectively,with diagnostic odds ratio=26(95%CI:13-52),SROC=0.87(95%CI:0.84-0.90),which showed superiority of diagnostic efficacy compared to WLI.CONCLUSION Our results showed LCI can improve efficacy of diagnosis on H.pylori infection,which represents a useful endoscopic evaluation modality for clinical practice.

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.

Artificial intelligence technique in detection of early esophageal cancer

Due to the rapid progression and poor prognosis of esophageal cancer(EC),the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients.However,the endoscopic detection of early EC,especially Barrett's dysplasia or squamous epithelial dysplasia,is difficult.Therefore,the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence(AI).Deep learning(DL)has brought about breakthroughs in processing images,videos,and other aspects,whereas convolutional neural networks(CNNs)have shone lights on detection of endoscopic images and videos.Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia.When AI technique comes to the pathological diagnosis,borderline lesions that are difficult to determine may become easier than before.In gene diagnosis,due to the lack of tissue specificity of gene diagnostic markers,they can only be used as supplementary measures at present.In predicting the risk of cancer,there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.

Cystic pancreatic lesions,the endless dilemma

Cystic pancreatic lesions involve a wide variety of pathological entities that include neoplastic and non-neoplastic lesions.The proper diagnosis,differentiation,and staging of these cystic lesions are considered a crucial issue in planning further management.There are great challenges for their diagnostic models.In our time,new emerging methods for this diagnosis have been discovered.Endoscopic ultrasonography-guided fine-needle aspiration cytology with chemical and molecular analysis of cyst fluid and EUS-guided fine needlebased confocal laser endomicroscopy,through the needle microforceps biopsy,and single-operator cho-langioscopy/pancreatoscopy are promising methods that have been used in the diagnosis of cystic pancreatic lesions.Hereby we discuss the diagnosis of cystic pancreatic lesions and the benefits of various diagnostic models.

Goblet cell carcinoid of the appendix:Six case reports

BACKGROUND Goblet cell carcinoid(GCC)of the appendix is a rare tumor characterized by neuroendocrine and adenocarcinoma features.Accurate preoperative diagnosis is very difficult,with most patients complaining mainly of abdominal pain.Computed tomography shows swelling of the appendix,so diagnosis is usually made incidentally after appendectomy based on a preoperative diagnosis of appendicitis.Even if a patient undergoes preoperative colonoscopy,accurate endoscopic diagnosis is very difficult because GCC shows a submucosal growth pattern with invasion of the appendiceal wall.CASE SUMMARY Between 2017 and 2022,6 patients with GCC were treated in our hospital.The presenting complaint for 5 of these 6 patients was abdominal pain.All 5 patients underwent appendectomy,including 4 for a preoperative diagnosis of appendicitis and the other for diagnosis and treatment of an appendiceal tumor.The sixth patient presented with vomiting and underwent ileocecal resection for GCC diagnosed from preoperative biopsy.Although 2 patients with GCC underwent colonoscopy,no neoplastic changes were identified.Two of the six patients showed lymph node metastasis on pathological examination.As of the last followup(median:15 mo),all cases remained alive without recurrence.CONCLUSION As preoperative diagnosis of GCC is difficult,this possibility must be considered during surgical treatments for presumptive appendicitis.