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This article, titled "Intracavitary vacuum therapy in the treatment of anastomotic leak: knowing when to stop is also crucial!", discusses a case study involving a 62-year-old male who underwent a total gastrectomy for gastric cancer. The patient developed sepsis within 72 hours and was found to have an anastomotic leak. Endoluminal vacuum therapy (EVT) was used to treat the leak, but after the third replacement, the patient experienced hemorrhage. EVT was then stopped due to the presence of tissue granulation and potential complications. The patient continued with medical treatment and one week later, endoscopy revealed complete closure of the dehiscence. This case emphasizes the importance of recognizing when to discontinue EVT, as the presence of granulation tissue can still allow for closure of a large-sized cavity. [Extracted from the article]

1. As the use of antibiotics as growth promoters has been banned in many regions, there has been an on-going search for possible alternative compounds, such as prebiotics and organic acids. 2. This study was conducted to investigate the influence of liquid whey (LW) and organic acid (ACIDAL® ML) supplementation on performance, eggs characteristics, gut pH and health status in laying hens. 3. Seven hundred and fifty, Isa Brown chicks were randomly assigned to five treatments groups (n = 150) and each treatment had five replicates of 30 birds each. The birds were reared for 48 weeks. The treatments were administered in the drinking water at doses of: 250 ml/l of LW (Lacto25), 500 ml/l of LW (Lacto50) or 1 ml/l of ACIDAL® ML (Aci). A positive control group (T+) was treated with 500 mg/l of Tetracolivit (an antibiotic). The negative control group (T-) did not receive any treatment in the drinking water. 4. Administration of LW or ACIDAL® ML in the drinking water reduced (P < 0.05) the pH in the crop, proventriculus, ileum and caeca, as well as total coliform bacteria and E. coli, but increased Lactobacillus spp. in the ileum and caecum, compared to the negative control. 5. Oviposition was earlier in the birds in both the Lacto50 and Aci groups. The weight of birds at first lay and point of lay in the four treated groups was higher than those in negative control group. Furthermore, egg production was increased by 10.44% in birds receiving Lacto25, but the weight and quality traits were unaffected, while the egg shell ratio was higher in the Aci group compared to the other treatments. 6. The data indicated that addition of LW or ACIDAL® ML improved hens' performance by modifying gut pH and microflora. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy.The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted.Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18).The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: During the last decade, the management of gastric intestinal metaplasia (GIM) has been addressed by several distinct international evidence-based guidelines. In this review, we aimed to synthesise these guidelines and provide clinicians with a global perspective of the current recommendations for managing patients with GIM, as well as highlight evidence gaps that need to be addressed with future research., Design: We conducted a systematic review of the literature for guidelines and consensus statements published between January 2010 and February 2023 that address the diagnosis and management of GIM., Results: From 426 manuscripts identified, 16 guidelines were assessed. There was consistency across guidelines regarding the purpose of endoscopic surveillance of GIM, which is to identify prevalent neoplastic lesions and stage gastric preneoplastic conditions. The guidelines also agreed that only patients with high-risk GIM phenotypes (eg, corpus-extended GIM, OLGIM stages III/IV, incomplete GIM subtype), persistent refractory Helicobacter pylori infection or first-degree family history of gastric cancer should undergo regular-interval endoscopic surveillance. In contrast, low-risk phenotypes, which comprise most patients with GIM, do not require surveillance. Not all guidelines are aligned on histological staging systems. If surveillance is indicated, most guidelines recommend a 3-year interval, but there is some variability. All guidelines recommend H. pylori eradication as the only non-endoscopic intervention for gastric cancer prevention, while some offer additional recommendations regarding lifestyle modifications. While most guidelines allude to the importance of high-quality endoscopy for endoscopic surveillance, few detail important metrics apart from stating that a systematic gastric biopsy protocol should be followed. Notably, most guidelines comment on the role of endoscopy for gastric cancer screening and detection of gastric precancerous conditions, but with high heterogeneity, limited guidance regarding implementation, and lack of robust evidence., Conclusion: Despite heterogeneous populations and practices, international guidelines are generally aligned on the importance of GIM as a precancerous condition and the need for a risk-stratified approach to endoscopic surveillance, as well as H. pylori eradication when present. There is room for harmonisation of guidelines regarding (1) which populations merit index endoscopic screening for gastric cancer and GIM detection/staging; (2) objective metrics for high-quality endoscopy; (3) consensus on the need for histological staging and (4) non-endoscopic interventions for gastric cancer prevention apart from H. pylori eradication alone. Robust studies, ideally in the form of randomised trials, are needed to bridge the ample evidence gaps that exist., Competing Interests: Competing interests: No conflicts of interest are declared by MD-R, SS, HE-S, MB, NU, HT, LGC, DL, EL, AR, J-YF, LM, JB, EJK, EME-O. PM is a member of advisory board/lecturer of Aboca, Alfasigma, Allergosan, Bayer, Biocodex, Menarini advisory boards/lectures., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background/aims: Endoscopic screening for gastric cancer (GC) is not recommended in low-intermediate incidence countries. Artificial intelligence (AI) has high accuracy in GC detection and might increase the cost-effectiveness of screening strategies. We aimed to assess the cost-effectiveness of AI for GC detection in settings with different GC incidence and different accuracies of AI systems., Methods: Cost-effectiveness analysis (using Markov model) comparing different screening strategies (no screening versus single esophagogastroduodenoscopy (EGD) at 50 years versus stand-alone EGD every 5/10 years versus combined EGD and screening colonoscopy once or twice per decade in Netherlands, Italy and Portugal) with variable AI accuracy settings. The primary outcome was the incremental cost-effectiveness ratio of the different strategies versus no screening. Deterministic and probabilistic sensitivity analyses were conducted., Results: Without AI, one single EGD at 50 years (Netherlands, Italy, Portugal), EGD combined with screening colonoscopy once per decade (Italy and Portugal) and EGD combined with screening colonoscopy twice per decade (Portugal) are cost-effective when compared with no screening. If AI increases the accuracy of EGD by at least 1% in comparison to the accuracy of white-light endoscopy accuracy (89%), combined screening twice per decade also becomes cost-effective in Italy. If AI accuracy reaches at least 96%, combined screening once per decade is also cost-effective in the Netherlands., Discussion: In European countries, AI-assisted EGD may improve the cost-effectiveness of GC screening with combined EGD and screening colonoscopy. The actual effect of AI on cost-effectiveness may vary dependent on the accuracy and costs of the AI system., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To evaluate the risk factors for lymph node metastasis (LNM) after a non-curative (NC) gastric endoscopic submucosal dissection (ESD) and to validate and eventually refine the eCura scoring system in the Western setting. Also, to assess the rate and risk factors for parietal residual disease., Design: Retrospective multicentre multinational study of prospectively collected registries from 19 Western centres. Patients who had been submitted to surgery or had at least one follow-up endoscopy were included. The eCura system was applied to assess its accuracy in the Western setting, and a modified version was created according to the results (W-eCura score). The discriminative capacities of the eCura and W-eCura scores to predict LNM were assessed and compared., Results: A total of 314 NC gastric ESDs were analysed (72% high-risk resection (HRR); 28% local-risk resection). Among HRR patients submitted to surgery, 25% had parietal disease and 15% had LNM in the surgical specimen. The risk of LNM was significantly different across the eCura groups (areas under the receiver operating characteristic curve (AUC-ROC) of 0.900 (95% CI 0.852 to 0.949)). The AUC-ROC of the W-eCura for LNM (0.916, 95% CI 0.870 to 0.961; p=0.012) was significantly higher compared with the original eCura. Positive vertical margin, lymphatic invasion and younger age were associated with a higher risk of parietal residual lesion in the surgical specimen., Conclusion: The eCura scoring system may be applied in Western countries to stratify the risk of LNM after a gastric HRR. A new score is proposed that may further decrease the number of unnecessary surgeries., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable inter-fold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.Clinical relevance- Enhanching a clinician's ability to detect and localize intestinal metaplasia can be a crucial tool for gastric cancer management policies.

This work focuses on detection of upper gas-trointestinal (GI) landmarks, which are important anatomical areas of the upper GI tract digestive system that should be photodocumented during endoscopy to guarantee a complete examination. The aim of this work consisted in testing new automatic algorithms, specifically based on convolutional neural network (CNN) systems, able to detect upper GI landmarks, that can help to avoid the presence of blind spots during esophagogastroduodenoscopy. We tested pre-trained CNN architectures, such as the ResNet-50 and VGG-16, in conjunction with different training approaches, including the use of class weights, batch normalization, dropout, and data augmentation. The ResNet-50 model trained with class weights was the best performing CNN, achieving an accuracy of 71.79% and a Mathews Correlation Coefficient (MCC) of 65.06%. The combination of supervised and unsupervised learning was also explored to increase classification performance. In particular, convolutional autoencoder architectures trained with unlabeled GI images were used to extract representative features. Such features were then concatenated with those extracted by the pre-trained ResNet-50 architecture. This approach achieved a classification accuracy of 72.45% and an MCC of 65.08%. Clinical relevance- Esophagogastroduodenoscopy (EGD) photodocumentation is essential to guarantee that all areas of the upper GI system are examined avoiding blind spots. This work has the objective to help the EGD photodocumentation monitorization by testing new CNN-based systems able to detect EGD landmarks.

This study aimed to build convolutional neural network (CNN) models capable of classifying upper endoscopy images, to determine the stage of infection in the development of a gastric cancer. Two different problems were covered. A first one with a smaller number of categorical classes and a lower degree of detail. A second one, consisting of a larger number of classes, corresponding to each stage of precancerous conditions in the Correa's cascade. Three public datasets were used to build the dataset that served as input for the classification tasks. The CNN models built for this study are capable of identifying the stage of precancerous conditions/lesions in the moment of an upper endoscopy. A model based on the DenseNet169 architecture achieved an average accuracy of 0.72 in discriminating among the different stages of infection. The trade-off between detail in the definition of lesion classes and classification performance has been explored. Results from the application of Grad CAMs to the trained models show that the proposed CNN architectures base their classification output on the extraction of physiologically relevant image features. Clinical relevance- This research could improve the accuracy of upper endoscopy exams, which have margin for improvement, by assisting doctors when analysing the lesions seen in patient's images.

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Background: Estimates on miss rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper GI endoscopy are not fully established owing to the lack of infrastructure to measure endoscopists' competence. We aimed to assess endoscopists' accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies., Methods: Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC)., Results: Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists' sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %-84 %) and 79 % (95 %CI 76 %-81 %), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93-0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88-0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84-0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %-89 %] vs. 75 % [95 %CI 72 %-78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %-87 %] vs. 71 % [95 %CI 67 %-75 %])., Conclusion: We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence., Competing Interests: J.J.G.H.M. Bergman has received grants and personal fees from Olympus, Fujifilm, and Pentax Endoscopy outside of the submitted work. P. Sharma has received personal fees from Olympus and Boston Scientific, and grants from CDx, US Endoscopy, Medtronic, Ironwood, Erbe, and Fujifilm outside of the submitted work. C. Hassan has received personal fees from Medtronic, Fujifilm, and Olympus outside of the submitted work. M. Dinis-Ribeiro has received grants from Olympus and Fujifulm outside of the submitted work. Y. Mori has received personal fees from Olympus Corp. and Cybernet System Corp. outside of the submitted work. The remaining authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: One of the aims of the European Society of Gastrointestinal Endoscopy (ESGE) is to encourage high quality endoscopic research at a European level. In 2016, the ESGE research committee published a set of research priorities. As endoscopic research is flourishing, we aimed to review the literature and determine whether endoscopic research over the last 4 years had managed to address any of our previously published priorities., Methods: As the previously published priorities were grouped under seven different domains, a working party with at least two European experts was created for each domain to review all the priorities under that domain. A structured review form was developed to standardize the review process. The group conducted an extensive literature search relevant to each of the priorities and then graded the priorities into three categories: (1) no longer a priority (well-designed trial, incorporated in national/international guidelines or adopted in routine clinical practice); (2) remains a priority (i. e. the above criterion was not met); (3) redefine the existing priority (i. e. the priority was too vague with the research question not clearly defined)., Results: The previous ESGE research priorities document published in 2016 had 26 research priorities under seven domains. Our review of these priorities has resulted in seven priorities being removed from the list, one priority being partially removed, another seven being redefined to make them more precise, with eleven priorities remaining unchanged. This is a reflection of a rapid surge in endoscopic research, resulting in 27 % of research questions having already been answered and another 27 % requiring redefinition., Conclusions: Our extensive review process has led to the removal of seven research priorities from the previous (2016) list, leaving 19 research priorities that have been redefined to make them more precise and relevant for researchers and funding bodies to target., Competing Interests: A. Anderloni has provided consultancy for Boston Scientific (2016 to date), Olympus (2018 to present), and Medtronic (2018–2019). M. Barthet has received a research grant from Boston Scientific (2016 to present). P. Bhandari has received educational event support from Olympus, speaker’s fees and grants from Pentax, Medtronic, Boston Scientific, and 3-D Matrix, and is on the advisory board and received a research grant from Fujifilm (all 2017 to present). M. Dinis-Ribeiro has provided consultancy for Medtronic (2020); he is co-Editor-in-Chief of Endoscopy. R. Haidry has received research funding from Medtronic (2013), Pentax (2013 to present), and Cook Endoscopy (2016 to present). H. Neumann has provided consultancy for Fujifilm, Sonoscope, Boston Scientific, and Medtronic (2019–2020). T. Ponchon has provided consultancy for Olympus, Boston Scientific, Norgine, and Ipsen (2020 to present); his department has received research funding from Olympus and Boston Scientific (2020 to present). A. Repici has received research grants and consultancy fees from Boston Scientific, Fujifilm, Medtronic, and Erbe. P.D. Siersema receives research support from Pentax, The eNose company, Norgine, Motus GI, and MicroTech; he is Editor-in-Chief of Endoscopy. C. Spada has provided consultancy for Medtronic, AlfaSigma, Norgine, Pentax, and Olympus (2016 to present). P.A. Testoni’s department has received meeting sponsorship and trials support from Pentax (2000 to present). E. Albeniz, G. Antonelli, L. Diogo, L. Fuccio, C. Hassan, G. Longcroft-Wheaton, P. Pimentel-Nunes, M. Pioche, and R. Sidhu declare that they have no conflict of interest., (European Society of Gastrointestinal Endoscopy. All rights reserved.)

Objective: Early diagnosis of gastric cancer may be achieved through surveillance of patients with extensive gastric intestinal metaplasia (eGIM). However, diagnosis of eGIM generally implies histology. We aimed at determining the accuracy of high-resolution endoscopy with light-narrow band imaging (NBI) to assess the presence of eGIM on a per-patient basis., Material and Methods: Prospective cohort of 60 patients divided into two groups: derivation cohort (n = 25) to evaluate the reliability and validity, and a real-time validation group (n = 35). In the derivation group, six endoscopists with two levels of expertise were asked to estimate the grade of GIM based in endoscopic images (white light endoscopy, light-NBI and amplification/near focus). In the real-time validation set, experienced endoscopists were asked to similarly record their real-time optical diagnosis. Histology was then considered as the gold standard., Results: In the derivation group diagnosis accuracy was 60% with WLE (non-expert 59% vs. 61% experts), increasing to 73% after NBI magnification (non-expert 63% vs. 83% expert, p < 0.05). Moreover, proportion of agreement with histology was 83%, with a correct diagnosis of eGIM in 87% for experienced observers. In the real-time group experts obtained 89% global diagnostic accuracy correctly identifying 91% of the eGIM. The sensitivity, specificity, LR + and LR- of real-time endoscopic diagnosis of eGIM was 0.92 (CI95%:0.67-0.99), 0.96 (0.79-0.99), 21.1 (3.08-144) and 0.09 (0.013-0.57)., Conclusion: For the first time the reliability of high-resolution endoscopy with light-NBI for extension of GIM is described. Our results suggest that more than 90% of individuals at risk could be identified without the need for biopsies, simplifying the current recommendations.