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BACKGROUND : Timely detection of neoplasia in Barrett's esophagus (BE) remains challenging. While computer-aided detection (CADe) systems have been developed to assist endoscopists, their effectiveness depends heavily on the quality of the endoscopic procedure. This study introduces a novel computer-aided quality (CAQ) system for BE, evaluating its stand-alone performance and integration with a CADe system. METHOD : The CAQ system was developed using 7,463 images from 359 BE patients. It assesses objective quality parameters (e. g., blurriness, illumination) and subjective parameters (mucosal cleanliness, esophageal expansion) and can exclude low-quality images when integrated with a CADe system.To evaluate CAQ stand-alone performance, the Endoscopic Image Quality test set, consisting of 647 images from 51 BE patients across 8 hospitals, was labeled for objective and subjective quality. To assess the benefit of the CAQ system as a preprocessing filter of a CADe system, the Barrett CADe test set was developed. It consisted of 956 video frames from 62 neoplastic patients and 557 frames from 35 non-dysplastic patients, in 12 Barrett referral centers. RESULTS : As stand-alone tool, the CAQ system achieved Cohen's Kappa scores of 0.73, 0.91, and 0.89 for objective quality, mucosal cleanliness, and esophageal expansion, comparable to inter-annotator scores of 0.73, 0.93, and 0.83. As preprocessing filter, the CAQ system improved CADe sensitivity from 82 % to 90 % and AUC from 87 % to 91 %, while maintaining specificity at 75 %. CONCLUSION : This study presents the first CAQ system for automated quality control in BE. The system effectively distinguishes poorly from well-visualized mucosa and enhances neoplasia detection when integrated with CADe., Competing Interests: J.J.G.H.M. Bergman reports financial support for institutional review board-approved research from Medtronic, Olympus, Castle Sciences, CDx Diagnostics, Cyted, Lucid, Fractyl Inc., Endogenex, Digma Medical, and Aqua Medical, and consultancies for Olympus, Fractyl, and Endogenex. R.E. Pouw provided endoscopy images for an endoscopy atlas for Olympus, with payment made directly to the institution. M.R. Jong, T.J.M. Jaspers, R.A.H. van Eijck van Heslinga, J.B. Jukema, C.H.J. Kusters, T.G.W. Boers, L.C. Duits, P.H.N. de With, F. van der Sommen, and A.J. de Groof declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Background:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the diversity in post-processing enhancement settings used in endoscopy units. We evaluated the impact of post-processing enhancement settings on AI performance and tested specific data augmentation strategies to mitigate performance loss., Methods:  We used a computer-aided detection (CADe) system for Barrett's neoplasia (6223 images, 906 patients) and a computer-aided diagnosis (CADx) system for colorectal polyps (3288 images, 969 patients), both trained on datasets acquired with Olympus equipment and with limited variability in enhancement settings. The CAD systems were then tested across a wide range of test sets, which comprised the same images, but displayed with different enhancement settings. Both CAD systems were then retrained using image enhancement-based data augmentation. The performance of the adjusted CAD systems was evaluated on the same test sets., Results:  Both systems displayed substantial performance variability over a range of enhancement settings (CADe: 83 %-92 % sensitivity, 84 %-91 % specificity; CADx: 78 %-85 % sensitivity, 45 %-63 % specificity). After retraining, variability in sensitivity and specificity was reduced to 2 % ( P  < 0.001) and 1 %, respectively ( P  = 0.003) for CADe, and 2 % ( P  = 0.03) and 8 %, respectively ( P  = 0.19) for CADx., Conclusion:  The performance of endoscopic AI systems can vary substantially depending on post-processing enhancement settings of the endoscopy unit. Specific data augmentation can mitigate this performance loss., Competing Interests: J.J.G.H.M Bergman has received financial support for IRB-approved research from Medtronic, Olympus, Castle Sciences, CDx Diagnostics, Cyted, Lucid, Fractyl Inc., Endogenex, Digma Medical, and Aqua Medical, and is a consultant for Olympus, Fractyl, and Endogenex. E. Dekker has received endoscopic equipment on loan from FujiFilm; a research grant from FujiFilm; honoraria for consultancy from Olympus, FujiFilm, Ambu, InterVenn, and Exact Sciences; speaker fees from Olympus, GI Supply, Norgine, IPSEN, PAION, and FujiFilm; and is a supervisory board member of the eNose Company. M. van der Vlugt has received an honorarium for educational courses from Tillotts. P.H.N. de With has received financial support for IRB-approved research from Olympus. The remaining authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Background and Aims: Human-computer interaction, particularly the graphical user interface (GUI) for displaying detection results, is an important but underexplored aspect of CADe systems in endoscopy. The aim of this study was to study if the use of a bounding box GUI or a heatmap GUI results in different performance of endoscopists when using CADe for Barrett's neoplasia., Methods: Thirty-seven endoscopists from six countries with varying expertise assessed 70 Barrett's esophagus videos. All videos were evaluated by our previously developed CADe system and comprised, at some point, a CADe detection, irrespective if the video contained neoplasia or not. The study had two phases: initially, videos were shown with either a bounding box or heatmap; after a two-week wash-out, the same videos were reordered and displayed with the alternate GUI. Endoscopists marked perceived neoplastic lesions and biopsy sites, also noting their subjective GUI preference. Primary endpoints were objective classification and localization performance. Secondary endpoint was the subjective preference., Results: There was no statistically significant difference in classification performance when endoscopists were provided with the bounding box or the heatmap visualization (sensitivity 83% vs. 83%, p = 0.29; specificity 86% vs 86%, p =0.09). Also, the comparison of localization accuracy between the bounding box and heatmap methods showed no significant differences, with both methods yielding a median score of 97%. Subjectively, 23 endoscopists preferred the heatmap and 14 the bounding box (p=0.04)., Conclusions: Although endoscopists expressed a preference for the heatmap GUI, this was not associated with a statistical difference in performance outcomes., (Copyright © 2025 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based techniques in such safety-critical applications. While Convolutional Neural Networks (CNNs) have been the go-to architecture for endoscopic image analysis, recent successes of the Transformer architecture in computer vision raise the possibility to update this conclusion. To this end, we evaluate and compare clinically relevant performance, generalization and robustness of state-of-the-art CNNs and Transformers for neoplasia detection in Barrett's esophagus. We have trained and validated several top-performing CNNs and Transformers on a total of 10,208 images (2,079 patients), and tested on a total of 7,118 images (998 patients) across multiple test sets, including a high-quality test set, two internal and two external generalization test sets, and a robustness test set. Furthermore, to expand the scope of the study, we have conducted the performance and robustness comparisons for colonic polyp segmentation (Kvasir-SEG) and angiodysplasia detection (Giana). The results obtained for featured models across a wide range of training set sizes demonstrate that Transformers achieve comparable performance as CNNs on various applications, show comparable or slightly improved generalization capabilities and offer equally strong resilience and robustness against common image corruptions and perturbations. These findings confirm the viability of the Transformer architecture, particularly suited to the dynamic nature of endoscopic video analysis, characterized by fluctuating image quality, appearance and equipment configurations in transition from hospital to hospital. The code is made publicly available at: https://github.com/BONS-AI-VCA-AMC/Endoscopy-CNNs-vs-Transformers., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jacques J. Bergman reports financial support was provided by Olympus Corporation, Tokyo, Japan. Peter H.N. de With reports financial support was provided by Olympus Corporation, Tokyo, Japan. Fons van der Sommen reports financial support was provided by Olympus Corporation, Tokyo, Japan. Jacques J. Bergman reports financial support was provided by PENTAX Medical. Jacques J. Bergman reports financial support was provided by Medtronic. Jacques J. Bergman reports financial support was provided by Aqua Medical., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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)) 2025. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background and Aims: Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia., Methods: The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance., Results: Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P = .04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P < .001). CADx assistance increased endoscopists' confidence in characterization (P < .001). CADx performance was similar to that of the BE experts., Conclusions: CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance., Competing Interests: Disclosure The following authors disclosed financial relationships: M. Barret: research funding from Pentax Medical; consulting fees from Medtronic; medical training honorarium from Olympus; and board membership with Norgine and Ambu. J. J. Bergman: research support from Olympus Tokyo; and support for institutional review board (IRB)-approved research from Medtronic. T. Beyna: paid consultancy and lecture fees from Olympus and MicroTech; paid consultancy from Boston Scientific; and lecture fees from Fujifilm, Pentax, and Erbe. A. J. de Groof: research support from Olympus Tokyo. R. Mallant-Hent: consulting fees from Janssen. O. Pech: speaker honorarium from Boston Scientific, Medtronic, Fujifilm, Olympus, Aohua, Falk, and BMS. R. Pouw: consulting fees from Medtronic and MicroTech; and speaker fees from Pentax Medical. K. Ragunath: consulting fees from Olympus and Boston Scientific; research support from Olympus. F. van der Sommen: research support from Olympus Tokyo. P. de With: research support from Olympus Tokyo. B. Weusten: financial support for IRB-approved studies, speaker fees, and consulting fees from Pentax Medical; research support from Aqua Medical; financial support for IRB-approved studies from the St. Antonius research fund; and chair of the European Society of Gastrointestinal Endoscopy working group for the revision of the BE guideline. All other authors disclosed no financial relationships. This study received financial and logistical support from Olympus (Tokyo, Japan). Olympus did not have an active role in the design of the study, analysis of the results, or the writing of the manuscript., (Copyright © 2024 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background and Aims: This pilot study evaluated the performance of a recently developed computer-aided detection (CADe) system for Barrett's neoplasia during live endoscopic procedures., Methods: Fifteen patients with a visible lesion and 15 without were included in this study. A CAD-assisted workflow was used that included a slow pullback video recording of the entire Barrett's segment with live CADe assistance, followed by CADe-assisted level-based video recordings every 2 cm of the Barrett's segment. Outcomes were per-patient and per-level diagnostic accuracy of the CAD-assisted workflow, in which the primary outcome was per-patient in vivo CADe sensitivity., Results: In the per-patient analyses, the CADe system detected all visible lesions (sensitivity 100%). Per-patient CADe specificity was 53%. Per-level sensitivity and specificity of the CADe-assisted workflow were 100% and 73%, respectively., Conclusions: In this pilot study, detection by the CADe system of all potentially neoplastic lesions in Barrett's esophagus was comparable to that of an expert endoscopist. Continued refinement of the system may improve specificity. External validation in larger multicenter studies is planned. (Clinical trial registration number: NCT05628441.)., Competing Interests: Disclosure The authors disclosed the following financial relationships: J. Bergman: Research support from Olympus Tokyo; financial support for IRB approved research from Medtronic. R. Pouw: Consultant for Medtronic and MicroTech; speaker fees from Pentax Medical. F. van der Sommen: Research support from Olympus Tokyo. P. de With: Research support from Olympus Tokyo. J. de Groof: Research support from Olympus Tokyo. All other authors disclosed no financial relationships., (Copyright © 2024 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient outcomes. Over the last years, several artificial intelligence (AI) systems have emerged to assist the endoscopist. The primary focus of research has been computer aided detection (CADe). Several groups have succeeded in developing competitive models for neoplasia detection. Additionally, computer aided diagnosis (CADx) models have been developed for subsequent lesion characterization and assistance in clinical decision making. Future studies should focus on bridging the domain gap between academic development and integration in daily practice., Competing Interests: Conflict of interest The authors (Martijn Jong and Jeroen de Groof) hereby declare no conflict of interest regarding this review article entitled “Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus”. No relevant disclosures., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quality heavily relies on both the skills and experience of the endoscopist and the specifications of the system used for screening. Factors such as poor illumination, motion blur, and specific post-processing settings can significantly alter the quality and general appearance of these images. This so-called domain gap between the data used for developing the system and the data it encounters after deployment, and the impact it has on the performance of deep neural networks (DNNs) supportive endoscopic CAD systems remains largely unexplored. As many of such systems, for e.g. polyp detection, are already being rolled out in clinical practice, this poses severe patient risks in particularly community hospitals, where both the imaging equipment and experience are subject to considerable variation. Therefore, this study aims to evaluate the impact of this domain gap on the clinical performance of CADe/CADx for various endoscopic applications. For this, we leverage two publicly available data sets (KVASIR-SEG and GIANA) and two in-house data sets. We investigate the performance of commonly-used DNN architectures under synthetic, clinically calibrated image degradations and on a prospectively collected dataset including 342 endoscopic images of lower subjective quality. Additionally, we assess the influence of DNN architecture and complexity, data augmentation, and pretraining techniques for improved robustness. The results reveal a considerable decline in performance of 11.6% (±1.5) as compared to the reference, within the clinically calibrated boundaries of image degradations. Nevertheless, employing more advanced DNN architectures and self-supervised in-domain pre-training effectively mitigate this drop to 7.7% (±2.03). Additionally, these enhancements yield the highest performance on the manually collected test set including images with lower subjective quality. By comprehensively assessing the robustness of popular DNN architectures and training strategies across multiple datasets, this study provides valuable insights into their performance and limitations for endoscopic applications. The findings highlight the importance of including robustness evaluation when developing DNNs for endoscopy applications and propose strategies to mitigate performance loss., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jacques J. Bergman reports financial support was provided by Olympus Corporation, Tokyo, Japan. Peter H.N. de With reports financial support was provided by Olympus Corporation, Tokyo, Japan. Fons van der Sommen reports financial support was provided by Olympus Corporation, Tokyo, Japan. Jacques J. Bergman reports financial support was provided by PENTAX Medical. Jacques J. Bergman reports financial support was provided by Medtronic. Jacques J. Bergman reports financial support was provided by Aqua Medical., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a CADe system for early neoplasia in Barrett's oesophagus., Methods: The CADe system was first pretrained with ImageNet followed by domain-specific pretraining with GastroNet. We trained the CADe system on a dataset of 14 046 images (2506 patients) of confirmed Barrett's oesophagus neoplasia and non-dysplastic Barrett's oesophagus from 15 centres. Neoplasia was delineated by 14 Barrett's oesophagus experts for all datasets. We tested the performance of the CADe system on two independent test sets. The all-comers test set comprised 327 (73 patients) non-dysplastic Barrett's oesophagus images, 82 (46 patients) neoplastic images, 180 (66 of the same patients) non-dysplastic Barrett's oesophagus videos, and 71 (45 of the same patients) neoplastic videos. The benchmarking test set comprised 100 (50 patients) neoplastic images, 300 (125 patients) non-dysplastic images, 47 (47 of the same patients) neoplastic videos, and 141 (82 of the same patients) non-dysplastic videos, and was enriched with subtle neoplasia cases. The benchmarking test set was evaluated by 112 endoscopists from six countries (first without CADe and, after 6 weeks, with CADe) and by 28 external international Barrett's oesophagus experts. The primary outcome was the sensitivity of Barrett's neoplasia detection by general endoscopists without CADe assistance versus with CADe assistance on the benchmarking test set. We compared sensitivity using a mixed-effects logistic regression model with conditional odds ratios (ORs; likelihood profile 95% CIs)., Findings: Sensitivity for neoplasia detection among endoscopists increased from 74% to 88% with CADe assistance (OR 2·04; 95% CI 1·73-2·42; p<0·0001 for images and from 67% to 79% [2·35; 1·90-2·94; p<0·0001] for video) without compromising specificity (from 89% to 90% [1·07; 0·96-1·19; p=0·20] for images and from 96% to 94% [0·94; 0·79-1·11; ] for video; p=0·46). In the all-comers test set, CADe detected neoplastic lesions in 95% (88-98) of images and 97% (90-99) of videos. In the benchmarking test set, the CADe system was superior to endoscopists in detecting neoplasia (90% vs 74% [OR 3·75; 95% CI 1·93-8·05; p=0·0002] for images and 91% vs 67% [11·68; 3·85-47·53; p<0·0001] for video) and non-inferior to Barrett's oesophagus experts (90% vs 87% [OR 1·74; 95% CI 0·83-3·65] for images and 91% vs 86% [2·94; 0·99-11·40] for video)., Interpretation: CADe outperformed endoscopists in detecting Barrett's oesophagus neoplasia and, when used as an assistive tool, it improved their detection rate. CADe detected virtually all neoplasia in a test set of consecutive cases., Funding: Olympus., Competing Interests: Declaration of interests MB is a consultant for Medtronic and a board member for Norgine and Ambu, and reports funding from Pentax Medical and medical training from Olympus. JJB reports financial support for institutional review board-approved research from C2Therapeutics and Pentax Medical, Medtronic, and Aqua Medical. TB reports consulting fees from Olympus, Boston Scientific, and Microtech; lecture fees from Olympus, Fujifilm, Pentax, Microtech, ERBE, and Medtronic; payment for expert testimony from Olympus; and participation on a data safety monitoring board or advisory board at Olympus. RCM-H reports consultant fees from Janssen. OP reports speaker honorarium from Boston Scientific, Medtronic, Fujifilm, Olympus, Aohua, Falk, and BMS. REP reports consulting fees from Medtronic and Microtech and speaker fees from Pentax. KR reports consulting fees from Olympus and lecture fees from Olympus. BLAMW reports financial support from Pentax Medical and St Antonius Research Fund; research support from Aqua Medical; consulting fees from Pentax Medical; and speaker fees from Pentax Medical and is chair of the European Society of Gastrointestinal Endoscopy guideline working group for the revision of the Barrett's oesophagus guideline. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Endoscopic detection of early neoplasia in Barrett's esophagus is difficult. Computer Aided Detection (CADe) systems may assist in neoplasia detection. The aim of this study was to report the first steps in the development of a CADe system for Barrett's neoplasia and to evaluate its performance when compared with endoscopists., Methods: This CADe system was developed by a consortium, consisting of the Amsterdam University Medical Center, Eindhoven University of Technology, and 15 international hospitals. After pretraining, the system was trained and validated using 1.713 neoplastic (564 patients) and 2.707 non-dysplastic Barrett's esophagus (NDBE; 665 patients) images. Neoplastic lesions were delineated by 14 experts. The performance of the CADe system was tested on three independent test sets. Test set 1 (50 neoplastic and 150 NDBE images) contained subtle neoplastic lesions representing challenging cases and was benchmarked by 52 general endoscopists. Test set 2 (50 neoplastic and 50 NDBE images) contained a heterogeneous case-mix of neoplastic lesions, representing distribution in clinical practice. Test set 3 (50 neoplastic and 150 NDBE images) contained prospectively collected imagery. The main outcome was correct classification of the images in terms of sensitivity., Results: The sensitivity of the CADe system on test set 1 was 84%. For general endoscopists, sensitivity was 63%, corresponding to a neoplasia miss-rate of one-third of neoplastic lesions and a potential relative increase in neoplasia detection of 33% for CADe-assisted detection. The sensitivity of the CADe system on test sets 2 and 3 was 100% and 88%, respectively. The specificity of the CADe system varied for the three test sets between 64% and 66%., Conclusion: This study describes the first steps towards the establishment of an unprecedented data infrastructure for using machine learning to improve the endoscopic detection of Barrett's neoplasia. The CADe system detected neoplasia reliably and outperformed a large group of endoscopists in terms of sensitivity., (© 2023 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

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 and Aims: Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett's esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia., Methods: The multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts., Results: Using the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%., Conclusions: We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.)., (Copyright © 2021 American Society for Gastrointestinal Endoscopy. All rights reserved.)

Patients with Barrett's esophagus are at an increased risk to develop esophageal cancer and, therefore, undergo regular endoscopic surveillance. Early detection of neoplasia enables endoscopic treatment, which improves outcomes. However, early Barrett's neoplasia is easily missed during endoscopic surveillance. This study investigates multidiameter single fiber reflectance spectroscopy (MDSFR) to improve Barrett's surveillance. Based on the concept of field cancerization, it may be possible to identify the presence of a neoplastic lesion from measurements elsewhere in the esophagus or even the oral cavity. In this study, MDSFR measurements are performed on non-dysplastic Barrett's mucosa, squamous mucosa, oral mucosa, and the neoplastic lesion (if present). Based on logistic regression analysis on the scattering parameters measured by MDSFR, a classifier is developed that can predict the presence of neoplasia elsewhere in the Barrett's segment from measurements on the non-dysplastic Barrett's mucosa (sensitivity 91%, specificity 71%, AUC = 0.77). Classifiers obtained from logistic regression analysis for the squamous and oral mucosa do not result in an AUC significantly different from 0.5., (© 2021 Wiley-VCH GmbH.)

Background and Aims: The endoscopic evaluation of narrow-band imaging (NBI) zoom imagery in Barrett's esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett's mucosa. Our aim was to demonstrate the feasibility of a deep-learning CAD system for tissue characterization of NBI zoom imagery in BE., Methods: The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic BE (NDBE) white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI zoom images with histologic correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed dataset of 59 neoplastic and 98 NDBE NBI zoom videos. Performance was evaluated using fourfold cross-validation. The primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI zoom videos., Results: The CAD system demonstrated accuracy, sensitivity, and specificity for detection of BE neoplasia using NBI zoom images of 84%, 88%, and 78%, respectively. In total, 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity, and specificity of the video-based CAD system were 83% (95% confidence interval [CI], 78%-89%), 85% (95% CI, 76%-94%), and 83% (95% CI, 76%-90%), respectively. The mean assessment speed was 38 frames per second., Conclusion: We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett's neoplasia on histologically confirmed unaltered NBI zoom videos with fast corresponding assessment time., (Copyright © 2021 American Society for Gastrointestinal Endoscopy. All rights reserved.)

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations., Competing Interests: Disclosure M.R. Struyvenberg, A.J. de Groof, F. van der Sommen, W.L. Curvers, P.H.N. de With: none declared. V.J.A. Konda: Pentax (grant). J.J. Bergman: Fujifilm, NinePoint Medical (research support); Fujifilm (speaking fees)., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Volumetric laser endomicroscopy (VLE) has been shown to improve detection of early neoplasia in Barrett's esophagus (BE). However, diagnostic performance using histopathology-correlated VLE regions of interest (ROIs) has not been adequately studied. We evaluated the diagnostic accuracy of VLE assessors for identification of early BE neoplasia in histopathology-correlated VLE ROIs. In total, 191 ROIs (120 nondysplastic and 71 neoplastic) from 50 BE patients were evaluated in a random order using a web-based module. All ROIs contained histopathology correlations enabled by VLE laser marking. Assessors were blinded to endoscopic BE images and histology. ROIs were first scored as nondysplastic or neoplastic. Level of confidence was assigned to the predicted diagnosis. Outcome measures were: (i) diagnostic performance of VLE assessors for identification of BE neoplasia in all VLE ROIs, defined as accuracy, sensitivity, and specificity; (ii) diagnostic performance of VLE assessors for only high level of confidence predictions; and (iii) interobserver agreement. Accuracy, sensitivity, and specificity for BE neoplasia identification were 79% (confidence interval [CI], 75-83), 75% (CI, 71-79), and 81% (CI, 76-86), respectively. When neoplasia was identified with a high level of confidence, accuracy, sensitivity, and specificity were 88%, 83%, and 90%, respectively. The overall strength of interobserver agreement was fair (k = 0.29). VLE assessors can identify BE neoplasia with reasonable diagnostic accuracy in histopathology-correlated VLE ROIs, and accuracy is enhanced when BE neoplasia is identified with high level of confidence. Future work should focus on renewed VLE image reviewing criteria and real-time automatic assessment of VLE scans., (© The Author(s) 2020. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus.)

Background and Aims: We assessed the preliminary diagnostic accuracy of a recently developed computer-aided detection (CAD) system for detection of Barrett's neoplasia during live endoscopic procedures., Methods: The CAD system was tested during endoscopic procedures in 10 patients with nondysplastic Barrett's esophagus (NDBE) and 10 patients with confirmed Barrett's neoplasia. White-light endoscopy images were obtained at every 2-cm level of the Barrett's segment and immediately analyzed by the CAD system, providing instant feedback to the endoscopist. At every level, 3 images were evaluated by the CAD system. Outcome measures were diagnostic performance of the CAD system per level and per patient, defined as accuracy, sensitivity, and specificity (ground truth was established by expert assessment and corresponding histopathology), and concordance of 3 sequential CAD predictions per level., Results: Accuracy, sensitivity, and specificity of the CAD system in a per-level analyses were 90%, 91%, and 89%, respectively. Nine of 10 neoplastic patients were correctly diagnosed. The single lesion not detected by CAD showed NDBE in the endoscopic resection specimen. In only 1 NDBE patient, the CAD system produced false-positive predictions. In 75% of all levels, the CAD system produced 3 concordant predictions., Conclusions: This is one of the first studies to evaluate a CAD system for Barrett's neoplasia during live endoscopic procedures. The system detected neoplasia with high accuracy, with only a small number of false-positive predictions and with a high concordance rate between separate predictions. The CAD system is thereby ready for testing in larger, multicenter trials. (Clinical trial registration number: NL7544.)., (Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background and Aims: Endoscopic recognition of early Barrett's neoplasia is challenging. Blue-light imaging (BLI) and linked-color imaging (LCI) may assist endoscopists in appreciation of neoplasia. Our aim was to evaluate BLI and LCI for visualization of Barrett's neoplasia in comparison with white-light endoscopy (WLE) alone, when assessed by nonexpert endoscopists., Methods: In this web-based assessment, corresponding WLE, BLI, and LCI images of 30 neoplastic Barrett's lesions were delineated by 3 expert endoscopists to establish ground truth. These images were then scored and delineated by 76 nonexpert endoscopists from 3 countries and with different levels of expertise, in 4 separate assessment phases with a washout period of 2 weeks. Assessments were as follows: assessment 1, WLE only; assessment 2, WLE + BLI; assessment 3, WLE + LCI; assessment 4, WLE + BLI + LCI. The outcomes were (1) appreciation of macroscopic appearance and ability to delineate lesions (visual analog scale [VAS] scores); (2) preferred technique (ordinal scores); and (3) assessors' delineation performance in terms of overlap with expert ground truth., Results: Median VAS scores for phases 2 to 4 were significantly higher than in phase 1 (P < .001). Assessors preferred BLI and LCI over WLE for appreciation of macroscopic appearance (P < .001) and delineation (P < .001). Linear mixed-effect models showed that delineation performance increased significantly in phase 4., Conclusions: The use of BLI and LCI has significant additional value for the visualization of Barrett's neoplasia when used by nonexpert endoscopists. Assessors appreciated the addition of BLI and LCI better than the use of WLE alone. Furthermore, this addition led to improved delineation performance, thereby allowing for better acquisition of targeted biopsy samples. (The Netherlands Trial Registry number: NL7541.)., (Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Volumetric laser endomicroscopy (VLE) is a balloon-based technique, which provides a circumferential near-microscopic scan of the esophageal wall layers, and has potential to improve Barrett's neoplasia detection. Interpretation of VLE imagery in Barrett's esophagus (BE) however is time-consuming and complex, due to a large amount of visual information and numerous subtle gray-shaded VLE images. Computer-aided detection (CAD), analyzing multiple neighboring VLE frames, might improve BE neoplasia detection compared to automated single-frame analyses. This study is to evaluate feasibility of automatic data extraction followed by CAD using a multiframe approach for detection of BE neoplasia. Prospectively collected ex-vivo VLE images from 29 BE-patients with and without early neoplasia were retrospectively analyzed. Sixty histopathology-correlated regions of interest (30 nondysplastic vs. 30 neoplastic) were assessed using different CAD systems. Multiple neighboring VLE frames, corresponding to 1.25 millimeter proximal and distal to each region of interest, were evaluated. In total, 3060 VLE frames were analyzed via the CAD multiframe analysis. Multiframe analysis resulted in a significantly higher median AUC (median level = 0.91) compared to single-frame (median level = 0.83) with a median difference of 0.08 (95% CI, 0.06-0.10), P < 0.001. A maximum AUC of 0.94 was reached when including 22 frames on each side using a multiframe approach. In total, 3060 VLE frames were automatically extracted and analyzed by CAD in 3.9 seconds. Multiframe VLE image analysis shows improved BE neoplasia detection compared to single-frame analysis. CAD with multiframe analysis allows for fast and accurate VLE interpretation, thereby showing feasibility of automatic full scan assessment in a real-time setting during endoscopy., (© The Author(s) 2019. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background & Aims: We aimed to develop and validate a deep-learning computer-aided detection (CAD) system, suitable for use in real time in clinical practice, to improve endoscopic detection of early neoplasia in patients with Barrett's esophagus (BE)., Methods: We developed a hybrid ResNet-UNet model CAD system using 5 independent endoscopy data sets. We performed pretraining using 494,364 labeled endoscopic images collected from all intestinal segments. Then, we used 1704 unique esophageal high-resolution images of rigorously confirmed early-stage neoplasia in BE and nondysplastic BE, derived from 669 patients. System performance was assessed by using data sets 4 and 5. Data set 5 was also scored by 53 general endoscopists with a wide range of experience from 4 countries to benchmark CAD system performance. Coupled with histopathology findings, scoring of images that contained early-stage neoplasia in data sets 2-5 were delineated in detail for neoplasm position and extent by multiple experts whose evaluations served as the ground truth for segmentation., Results: The CAD system classified images as containing neoplasms or nondysplastic BE with 89% accuracy, 90% sensitivity, and 88% specificity (data set 4, 80 patients and images). In data set 5 (80 patients and images) values for the CAD system vs those of the general endoscopists were 88% vs 73% accuracy, 93% vs 72% sensitivity, and 83% vs 74% specificity. The CAD system achieved higher accuracy than any of the individual 53 nonexpert endoscopists, with comparable delineation performance. CAD delineations of the area of neoplasm overlapped with those from the BE experts in all detected neoplasia in data sets 4 and 5. The CAD system identified the optimal site for biopsy of detected neoplasia in 97% and 92% of cases (data sets 4 and 5, respectively)., Conclusions: We developed, validated, and benchmarked a deep-learning computer-aided system for primary detection of neoplasia in patients with BE. The system detected neoplasia with high accuracy and near-perfect delineation performance. The Netherlands National Trials Registry, Number: NTR7072., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Endoscopic detection of early Barrett's esophagus-related neoplasia (BORN) is a challenge. We aimed to develop a web-based teaching tool for improving detection and delineation of BORN., Methods: We made high-definition digital videos during endoscopies of patients with BORN and non-dysplastic Barrett's esophagus. Three experts superimposed their delineations of BORN lesions on the videos using special tools. In phase one, 68 general endoscopists from 4 countries assessed 4 batches of 20 videos. After each batch, mandatory feedback compared the assessors' interpretations with those from experts. These data informed the selection of 25 videos for the phase 2 module, which was completed by 121 new assessors from 5 countries. A 5-video test batch was completed before and after scoring of the four 5-video training batches. Mandatory feedback was as in phase 1. Outcome measures were scores for detection, delineation, agreement delineation, and relative delineation of BORN., Results: A linear mixed-effect model showed significant sequential improvement for all 4 outcomes over successive training batches in both phases. In phase 2, median detection rates of BORN in the test batch increased by 30% (P < .001) after training. From baseline to the end of the study, there were relative increases in scores of 46% for detection, 129% for delineation, 105% for agreement delineation, and 106% for relative delineation (all, P < .001). Scores improved independent of assessors' country of origin or level of endoscopic experience., Conclusions: We developed a web-based teaching tool for endoscopic recognition of BORN that is easily accessible, efficient, and increases detection and delineation of neoplastic lesions. Widespread use of this tool might improve management of Barrett's esophagus by general endoscopists., (Copyright © 2019 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background and Aims: Endoscopic features of early neoplasia in Barrett's esophagus (BE) are subtle. Blue-light imaging (BLI) may improve visualization of neoplastic lesions. The aim of this study was to evaluate BLI in visualization of Barrett's neoplasia., Methods: Corresponding white-light endoscopy (WLE) and BLI images of 40 BE lesions were obtained prospectively and assessed by 6 international experts in 3 assessments. Each assessment consisted of overview and magnification images. Assessments were as follows: assessment 1, WLE only; assessment 2, BLI only; and assessment 3, corresponding WLE and BLI images. Outcome parameters were as follows: (1) appreciation of macroscopic appearance and surface relief (visual analog scale scores); (2) ability to delineate lesions (visual analog scale scores); (3) preferred technique for delineation (ordinal scores); and (4) quantitative agreement on delineations (AND/OR scores)., Results: Experts appreciated BLI significantly better than WLE for visualization of macroscopic appearance (median 8.0 vs 7.0, P < .001) and surface relief (8.0 vs 6.0, P < .001). For both overview and magnification images, experts appreciated BLI significantly better than WLE for ability to delineate lesions (8.0 vs 6.0, P < .001 and 8.0 vs 5.0, P < .001). There was no overall significant difference in AND/OR scores of WLE + BLI when compared with WLE, yet agreement increased significantly with WLE + BLI for cases with a low baseline AND/OR score on WLE, both in overview (mean difference, 0.15; P = .015) and magnification (mean difference, 0.10; P = .01)., Conclusions: BLI has additional value for visualization of BE neoplasia. Experts appreciated BLI better than WLE for visualization and delineation of BE neoplasia. Quantitative agreement increased significantly when BLI was offered next to WLE for lesions that were hard to delineate with WLE alone. (ISRCTN registry study ID: ISRCTN15916689.)., (Copyright © 2019 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background: Treatment of patients with ileocaecal Crohn's disease who have not responded to conventional therapy is commonly scaled up to biological agents, but surgery can also offer excellent short-term and long-term results. We compared laparoscopic ileocaecal resection with infliximab to assess how they affect health-related quality of life., Methods: In this randomised controlled, open-label trial, in 29 teaching hospitals and tertiary care centres in the Netherlands and the UK, adults with non-stricturing, ileocaecal Crohn's disease, in whom conventional therapy has failed were randomly allocated (1:1) by an internet randomisation module with biased-coin minimisation for participating centres and perianal fistula to receive laparoscopic ileocaecal resection or infliximab. Eligible patients were aged 18-80 years, had active Crohn's disease of the terminal ileum, and had not responded to at least 3 months of conventional therapy with glucocorticosteroids, thiopurines, or methotrexate. Patients with diseased terminal ileum longer than 40 cm or abdominal abscesses were excluded. The primary outcome was quality of life on the Inflammatory Bowel Disease Questionnaire (IBDQ) at 12 months. Secondary outcomes were general quality of life, measured by the Short Form-36 (SF-36) health survey and its physical and mental component subscales, days unable to participate in social life, days on sick leave, morbidity (additional procedures and hospital admissions), and body image and cosmesis. Analyses of the primary outcome were done in the intention-to-treat population, and safety analyses were done in the per-protocol population. This trial is registered at the Dutch Trial Registry (NTR1150)., Findings: Between May 2, 2008, and October 14, 2015, 73 patients were allocated to have resection and 70 to receive infliximab. Corrected for baseline differences, the mean IBDQ score at 12 months was 178·1 (95% CI 171·1-185·0) in the resection group versus 172·0 (164·3-179·6) in the infliximab group (mean difference 6·1 points, 95% CI -4·2 to 16·4; p=0·25). At 12 months, the mean SF-36 total score was 112·1 (95% CI 108·0-116·2) in the resection group versus 106·5 (102·1-110·9) in the infliximab group (mean difference 5·6, 95% CI -0·4 to 11·6), the mean physical component score was 47·7 (45·7-49·7) versus 44·6 (42·5-46·8; mean difference 3·1, 4·2 to 6·0), and the mean mental component score was 49·5 (47·0-52·1) versus 46·1 (43·3-48·9; mean difference 3·5, -0·3 to 7·3). Mean numbers of days of sick leave were 3·4 days (SD 7·1) in the resection group versus 1·4 days (4·7) in the infliximab group (p<0·0001), days not able to take part in social life were 1·8 days (6·3) versus 1·1 days (4·5; p=0·20), days of scheduled hospital admission were 6·5 days (3·8) versus 6·8 days (3·2; p=0·84), and the number of patients who had unscheduled hospital admissions were 13 (18%) of 73 versus 15 (21%) of 70 (p=0·68). Body-image scale mean scores in the patients who had resection were 16·0 (95% CI 15·2-16·8) at baseline versus 17·8 (17·1-18·4) at 12 months, and cosmetic scale mean scores were 17·6 (16·6-18·6) versus 18·6 (17·6-19·6). Surgical intervention-related complications classified as IIIa or worse on the Clavien-Dindo scale occurred in four patients in the resection group. Treatment-related serious adverse events occurred in two patients in the infliximab group. During a median follow-up of 4 years (IQR 2-6), 26 (37%) of 70 patients in the infliximab group had resection, and 19 (26%) of 73 patients in the resection group received anti-TNF., Interpretation: Laparoscopic resection in patients with limited (diseased terminal ileum <40 cm), non-stricturing, ileocaecal Crohn's disease in whom conventional therapy has failed could be considered a reasonable alternative to infliximab therapy., Funding: Netherlands Organisation for Health Research and Development., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background and Aim: Volumetric laser endomicroscopy (VLE) provides a circumferential scan of the esophageal wall layers and has potential to improve detection of neoplasia in Barrett's esophagus (BE). The novel VLE laser marking system enables direct in vivo marking of suspicious areas as identified on VLE. These laser marked areas can subsequently be targeted for biopsies. The aim was to evaluate the visibility and positional accuracy of laser marks (LMs) in different esophageal tissue types on white light endoscopy (WLE) and VLE., Methods: Patients with BE with or without neoplasia underwent imaging with VLE. Protocol refinements were practiced in a learning phase. In the second phase, visibility of LMs was assessed by random marking in squamous, BE, and gastric tissue. In phase 3, positional accuracy of the LMs was tested by identifying and laser marking surrogate targets (endoscopically placed cautery marks). In the final phase, the most suspicious areas for neoplasia were identified in each patient using VLE, targeted by LMs, and biopsy samples subsequently obtained., Results: Sixteen patients with BE were included (14 men; median age, 68 years), 1 of whom was included twice in different study phases. Worst histologic diagnoses were 9 non-dysplastic Barrett's esophagus (NDBE), 3 low-grade dysplasia (LGD), 4 high-grade dysplasia (HGD), and 1 early adenocarcinoma (EAC). In total, 222 LMs were placed, of which 97% was visible on WLE. All LMs were visible on VLE directly after marking, and 86% could be confirmed during post hoc analysis. LM targeting was successful with positional accuracy in 85% of cautery marks. Inaccurate targeting was caused by system errors or difficult cautery mark visualization on VLE. In the final phase (5 patients), 18 areas suspicious on VLE were identified, which were all successfully targeted by LMs (3 EAC, 3 HGD, 1 LGD, and 11 NDBE). Mean VLE procedure time was 22 minutes (±6 minutes standard deviation); mean endoscopy time was 56 minutes (±17 minutes). No adverse events were reported., Conclusions: This first-in-human study of VLE-guided laser marking was found to be feasible and safe in 17 procedures. Most LMs were visible on WLE and VLE. Targeting VLE areas of interest proved to be highly successful. VLE-guided laser marking may improve the detection and delineation of Barrett's neoplasia in the future., (Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background: Minimally invasive endovascular aneurysm repair (EVAR) could be a surgical technique that improves outcome of patients with ruptured abdominal aortic aneurysm (rAAA). The aim of this study was to analyse the cost-effectiveness and cost-utility of EVAR compared with standard open repair (OR) in the treatment of rAAA, with costs per 30-day and 6-month survivor as outcome parameters., Methods: Resource use was determined from the Amsterdam Acute Aneurysm (AJAX) trial, a multicentre randomized trial comparing EVAR with OR in patients with rAAA. The analysis was performed from a provider perspective. All costs were calculated as if all patients had been treated in the same hospital (Onze Lieve Vrouwe Gasthuis, teaching hospital)., Results: A total of 116 patients were randomized. The 30-day mortality rate was 21 per cent after EVAR and 25 per cent for OR: absolute risk reduction (ARR) 4·4 (95 per cent confidence interval (c.i.) -11·0 to 19·7) per cent. At 6 months, the total mortality rate for EVAR was 28 per cent, compared with 31 per cent among those assigned to OR: ARR 2·4 (-14·2 to 19·0) per cent. The mean cost difference between EVAR and OR was €5306 (95 per cent c.i. -1854 to 12,659) at 30 days and €10,189 (-2477 to 24,506) at 6 months. The incremental cost-effectiveness ratio per prevented death was €120,591 at 30 days and €424,542 at 6 months. There was no significant difference in quality of life between EVAR and OR. Nor was EVAR superior regarding cost-utility., Conclusion: EVAR may be more effective for rAAA, but its increased costs mean that it is unaffordable based on current standards of societal willingness-to-pay for health gains., (© 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.)

Alzheimer disease (AD) is associated with aberrant processing of the amyloid precursor protein (APP) by γ-secretase, via an unknown mechanism. We recently showed that presenilin-1 and -2, the catalytic components of γ-secretase, and γ-secretase activity itself, are highly enriched in a subcompartment of the endoplasmic reticulum (ER) that is physically and biochemically connected to mitochondria, called mitochondria-associated ER membranes (MAMs). We now show that MAM function and ER-mitochondrial communication-as measured by cholesteryl ester and phospholipid synthesis, respectively-are increased significantly in presenilin-mutant cells and in fibroblasts from patients with both the familial and sporadic forms of AD. We also show that MAM is an intracellular detergent-resistant lipid raft (LR)-like domain, consistent with the known presence of presenilins and γ-secretase activity in rafts. These findings may help explain not only the aberrant APP processing but also a number of other biochemical features of AD, including altered lipid metabolism and calcium homeostasis. We propose that upregulated MAM function at the ER-mitochondrial interface, and increased cross-talk between these two organelles, may play a hitherto unrecognized role in the pathogenesis of AD.

Human mitochondrial complex I (CI) deficiency is associated with progressive neurological disorders. To better understand the CI pathomechanism, we here studied how deletion of the CI gene NDUFS4 affects cell metabolism. To this end we compared immortalized mouse embryonic fibroblasts (MEFs) derived from wildtype (wt) and whole-body NDUFS4 knockout (KO) mice. Mitochondria from KO cells lacked the NDUFS4 protein and mitoplasts displayed virtually no CI activity, moderately reduced CII, CIII and CIV activities and normal citrate synthase and CV (F(o)F(1)-ATPase) activity. Native electrophoresis of KO cell mitochondrial fractions revealed two distinct CI subcomplexes of ~830kDa (enzymatically inactive) and ~200kDa (active). The level of fully-assembled CII-CV was not affected by NDUFS4 gene deletion. KO cells exhibited a moderately reduced maximal and routine O(2) consumption, which was fully inhibited by acute application of the CI inhibitor rotenone. The aberrant CI assembly and reduced O(2) consumption in KO cells were fully normalized by NDUFS4 gene complementation. Cellular [NAD(+)]/[NADH] ratio, lactate production and mitochondrial tetramethyl rhodamine methyl ester (TMRM) accumulation were slightly increased in KO cells. In contrast, NDUFS4 gene deletion did not detectably alter [NADP(+)]/[NADPH] ratio, cellular glucose consumption, the protein levels of hexokinases (I and II) and phosphorylated pyruvate dehydrogenase (P-PDH), total cellular adenosine triphosphate (ATP) level, free cytosolic [ATP], cell growth rate, and reactive oxygen species (ROS) levels. We conclude that the NDUFS4 subunit is of key importance in CI stabilization and that, due to the metabolic properties of the immortalized MEFs, NDUFS4 gene deletion has only modest effects at the live cell level. This article is part of a special issue entitled: 17th European Bioenergetics Conference (EBEC 2012)., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Presenilin-1 (PS1) and -2 (PS2), which when mutated cause familial Alzheimer disease, have been localized to numerous compartments of the cell, including the endoplasmic reticulum, Golgi, nuclear envelope, endosomes, lysosomes, the plasma membrane, and mitochondria. Using three complementary approaches, subcellular fractionation, gamma-secretase activity assays, and immunocytochemistry, we show that presenilins are highly enriched in a subcompartment of the endoplasmic reticulum that is associated with mitochondria and that forms a physical bridge between the two organelles, called endoplasmic reticulum-mitochondria-associated membranes. A localization of PS1 and PS2 in mitochondria-associated membranes may help reconcile the disparate hypotheses regarding the pathogenesis of Alzheimer disease and may explain many seemingly unrelated features of this devastating neurodegenerative disorder.

Background: The Warburg phenotype in cancer cells has been long recognized, but there is still limited insight in the consecutive metabolic alterations that characterize its establishment. We obtained better understanding of the coupling between metabolism and malignant transformation by studying mouse embryonic fibroblast-derived cells with loss-of-senescence or H-RasV12/E1A-transformed phenotypes at different stages of oncogenic progression., Results: Spontaneous immortalization or induction of senescence-bypass had only marginal effects on metabolic profiles and viability. In contrast, H-RasV12/E1A transformation initially caused a steep increase in oxygen consumption and superoxide production, accompanied by massive cell death. During prolonged culture in vitro, cell growth rate increased gradually, along with tumor forming potential in in vitro anchorage-independent growth assays and in vivo tumor formation assays in immuno-deficient mice. Notably, glucose-to-lactic acid flux increased with passage number, while cellular oxygen consumption decreased. This conversion in metabolic properties was associated with a change in mitochondrial NAD+/NADH redox, indicative of decreased mitochondrial tricarboxic acid cycle and OXPHOS activity., Conclusion: The high rate of oxidative metabolism in newly transformed cells is in marked contrast with the high glycolytic rate in cells in the later tumor stage. In our experimental system, with cells growing under ambient oxygen conditions in nutrient-rich media, the shift towards this Warburg phenotype occurred as a step-wise adaptation process associated with augmented tumorigenic capacity and improved survival characteristics of the transformed cells. We hypothesize that early-transformed cells, which potentially serve as founders for new tumor masses may escape therapies aimed at metabolic inhibition of tumors with a fully developed Warburg phenotype.

Intracellular pathogenic organisms such as salmonellae and shigellae are able to evade the effects of many antibiotics because the drugs are not able to penetrate the plasma membrane. In addition, these bacteria may be able to transfer genes within cells while protected from the action of drugs. The primary mode by which virulence and antibiotic resistance genes are spread is bacterial conjugation. Salmonellae have been shown to be competent for conjugation in the vacuoles of cultured mammalian cells. We now show that the conjugation machinery is also functional in the mammalian cytosol. Specially constructed Escherichia coli strains expressing Shigella flexneri plasmid and chromosomal virulence factors for escape from vacuoles and synthesizing the invasin protein from Yersinia pseudotuberculosis to enhance cellular entry were able to enter 3T3 cells and escape from the phagocytic vacuole. One bacterial strain (the donor) of each pair to be introduced sequentially into mammalian cells had a conjugative plasmid. We found that this plasmid could be transferred at high frequency. Conjugation in the cytoplasm of cells may well be a general phenomenon.

We assessed the relationship between phosphocreatine (PCr) and creatine (Cr) content and creatine kinase (CK) activity in skeletal muscle of mice. The PCr and total Cr (tCr) concentrations, as well as CK activity, in hindlimb muscles of mice, with or without the cytosolic and mitochondrial isoforms of muscle creatine kinase (wild-type or CK--/-- mice), were determined by in vivo magnetic resonance (MR) spectroscopy and by biochemical means during postnatal growth and adulthood. In wild-type muscle the [tCr], PCr/ATP ratio and CK activity increased rapidly in the first 4-7 weeks. Remarkably, CK--/-- mice showed a similar increase in the PCr/ATP ratio during the first month in the presence of only minor brain-type BB-CK activity. Uptake of Cr in muscle was seemingly unrelated to CK activity as tCr increased in the same way in the muscles of both mouse types. At older ages the PCr/ATP ratio decreased in CK--/-- muscles, in contrast to wild-type where it still slowly increased, whereas [tCr] was similar for muscle of both mouse types. Using a new in vivo MR approach with application of [4-13C]Cr, a lower PCr/tCr ratio was also observed in CK--/-- muscle. From these data it follows that in vivo global ATP levels at rest are similar in the presence or absence of CK. Although Cr could still be converted to PCr in mature CK--/-- muscle, the immediate availability of PCr decreased, and PCr became partly inconvertible at older ages. Apparently, catalysis of the CK reaction by BB-CK, although significant in muscles of newborn mice, gradually declines to very low levels in adulthood. Part or all of this BB-CK may arise from satellite cells fusing with myotubes, a process that is most active during the first months of life. Finally, our observation that the MR and chemical assessment of muscle [tCr] and PCr/tCr ratio were similar for all mice does not support the existence of a significant MR-invisible or immobile pool of Cr, with a role for CK in this phenomenon.

Muscle function depends on an adequate ATP supply to sustain the energy consumption associated with Ca(2+) cycling and actomyosin sliding during contraction. In this regulation of energy homeostasis, the creatine kinase (CK) circuit for high energy phosphoryl transfer between ATP and phosphocreatine plays an important role. We earlier established a functional connection between the activity of the CK system and Ca(2+) homeostasis during depolarization and contractile activity of muscle. Here, we show how CK activity is coupled to the kinetics of spontaneous and electrically induced Ca(2+) transients in the sarcoplasm of myotubes. Using the UV ratiometric Ca(2+) probe Indo-1 and video-rate confocal microscopy in CK-proficient and -deficient cultured cells, we found that spontaneous and electrically induced transients were dependent on ryanodine-sensitive Ca(2+) release channels, sarcoplasmic/endoplasmic reticulum Ca(2+)-ATPase pumps, extracellular calcium, and functional mitochondria in both cell types. However, at increasing sarcoplasmic Ca(2+) load (induced by electrical stimulation at 0.1, 1, and 10 Hz), the Ca(2+) removal rate and the amount of Ca(2+) released per transient were gradually reduced in CK-deficient (but not wild-type) myotubes. We conclude that the CK/phosphocreatine circuit is essential for efficient delivery of ATP to the sarcoplasmic/endoplasmic reticulum Ca(2+)-ATPase pumps and thereby directly influences sarcoplasmic reticulum refilling and the kinetics of the sarcoplasmic Ca(2+) signals.

We have studied the mechanisms that regulate the remodeling of the glycolytic, mitochondrial and structural network of muscles of creatine kinase M (M-CK)/sarcomeric mitochondrial creatine kinase (ScCKmit) knockout mice by comparison of wild-type and mutant mRNA profiles on cDNA arrays. The magnitudes of changes in mRNA levels were most prominent in M-CK/ScCKmit (CK(-/-)) double mutants but did never exceed those of previously observed changes in protein level for any protein examined. In gastrocnemius of CK(-/-) mice we measured a 2.5-fold increase in mRNA level for mitochondrial encoded cytochrome c oxidase (COX)-III which corresponds to the increase in protein content. The level of the nuclear encoded mRNAs for COX-IV, H(+)-ATP synthase-C, adenine nucleotide translocator-1 and insulin-regulatable glucose transporter-4 showed a 1.5-fold increase, also in agreement with protein data. In contrast, no concomitant up-regulation in mRNA and protein content was detected for the mitochondrial inorganic phosphate-carrier, voltage-dependent anion channel and certain glycolytic enzymes. Our results reveal that regulation of transcript level plays an important role, but it is not the only principle involved in the remodeling of mitochondrial and cytosolic design of CK(-/-) muscles.

Skeletal muscles respond with high plasticity to pathobiological conditions or changes in physiological demand by remodeling cytoarchitectural and metabolic characteristics of individual myocytes. We have previously shown that muscles of mice without mitochondrial and/or cytosolic creatine kinases (ScCKmit(-/-) and/or M-CK(-/-)) partly compensate for the defect(s) by redirecting metabolic pathways and ultrastructural characteristics. Here, we show by semiquantitative Western blot analysis that the compensatory changes involve mutation- and fiber-type-specific coordinated regulation of divergent but functionally coupled groups of proteins. Fast-twitch gastrocnemius muscle of CK(--/--) mice display a two- to fourfold upregulation of mitochondrial cytochrome c oxidase, inorganic phosphate carrier, adenine nucleotide translocator, and voltage-dependent anion channel proteins. In parallel, cytosolic myoglobin is upregulated. Slow-twitch soleus muscle responds with changes in the glycolytic enzyme pattern, including a shift in lactate dehydrogenase isoenzyme composition. Adaptations in the network for oxidative adenosine triphosphate (ATP) production are already apparent at 17 days of age., (Copyright 2001 John Wiley & Sons, Inc.)

In order to map the autosomal dwarf (adw) locus in the chicken, 11 segregating families were created. Initially five of these families were used for a linkage experiment in which the genome was scanned with microsatellites using a technique called bulked segregant analysis. Subsequently animals from 11 families were typed individually for microsatellites that appeared to be linked. We were able to detect genetic linkage of the adw locus to five different microsatellite markers on chromosome 1, the closest showing a recombination fraction of only 0.03 (LOD score 32.12). In mice the phenotype pygmy shows a striking similarity to the autosomal dwarf phenotype in chickens, both having a disproportionately large head. The pygmy locus has been mapped on mouse chromosome 10 and found to represent a mutation in the gene coding for high-mobility group protein I-C (HMGI-C). Considering the synteny between regions of chicken chromosome 1, mouse chromosome 10 and human chromosome 12, and taking into account both the phenotypic characteristics and the mode of inheritance of the chicken adw and the mouse pygmy loci, the HMGI-C gene is a major candidate gene for the adw locus in the chicken. Fluorescence in situ hybridization of metaphase chromosomes with the chicken HMGI-C gene as a probe, showed that the chicken HMGI-C gene is indeed closely linked to marker LEI146 on chromosome 1.