Therapeutic Advances in Gastrointestinal Endoscopy最新文献

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.

Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning-based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis.

Results and conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation.

Plain language summary: Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

Data on prognostic factors associated with outcome following resection of perihilar cholangiocarcinoma vary. We sought to define and characterize current available evidence on prognostic factors associated with perihilar cholangiocarcinoma after resection. The PubMed, Embase, and Cochrane library were systematically searched for relevant studies published before December 2019. Prognostic factors were identified from multivariate regression analyses in studies. Only high-quality studies were included (Newcastle-Ottawa Scale > 6 stars). A total of 45 studies involving 7338 patients were analyzed. The meta-analysis demonstrated that serum bilirubin levels (hazard ratio: 1.76, 95% confidence interval: 1.27-2.44), serum CA19-9 levels (hazard ratio: 1.32, 95% confidence interval: 1.05-1.65), tumor size (hazard ratio: 1.27, 95% confidence interval: 1.04-1.55), major vascular involvement (hazard ratio: 1.61, 95% confidence interval: 1.09-2.38), distance metastasis (hazard ratio: 17.60, 95% confidence interval: 2.01-154.09), perioperative blood transfusion (hazard ratio: 1.36, 95% confidence interval: 1.15-1.62), T-stage (hazard ratio: 1.96, 95% confidence interval: 1.47-2.61), lymph node metastasis (hazard ratio: 2.06, 1.83-2.31), resection margin status (hazard ratio: 2.34, 95% confidence interval: 1.89-2.89), not-well histology differentiation (hazard ratio: 2.03, 95% confidence interval: 1.69-2.44), perineural invasion (hazard ratio: 2.37, 95% confidence interval: 1.59-3.55), and lymphovascular invasion (hazard ratio: 1.41, 95% confidence interval: 1.15-1.73) were prognostic factors for poorer overall survival. Adjuvant chemotherapy (hazard ratio: 0.37, 95% confidence interval: 0.25-0.55) had a positive effect on prolonged overall survival. In addition, positive resection margin status (hazard ratio: 1.96, 95% confidence interval: 1.47-2.61) and lymph node metastasis (hazard ratio: 2.06, 95% confidence interval: 1.83-2.31) were associated with poorer disease-free survival. The prognostic factors identified in the present meta-analysis can be used to characterize patients in clinical practice and enrich prognostic tools, which could be included in future trial designs and generate hypotheses to be tested in future research to promote personalized treatment.

Obesity remains a growing public health epidemic that has increased healthcare costs and related comorbidities. Current treatment guidelines encourage a multidisciplinary approach starting from patient selection, interventions, and long-term follow-up to maintain weight loss. However, these conservative interventions are largely ineffective at reducing body weight due to low adherence to the treatment regimen. Recently, endoscopic bariatric therapies have become an attractive alternative to traditional invasive bariatric surgeries due to their improved efficacy, safety, and cost-effectiveness. Endoscopic bariatric therapies include intragastric balloon placement, endoscopic sleeve gastroplasty, gastric bypass revision, and aspiration therapy. These procedures fall into two separate categories depending on the primary mechanism involved: restrictive or malabsorptive. Restrictive methods, such as the Orbera^® and ReShape™ intragastric balloons, increase satiation and delay gastric emptying while decreasing the amount of food that can be ingested. In contrast, malabsorptive devices, such as the EndoBarrier^®, interfere with the small intestine's ability to absorb food while restoring normal gastrointestinal hormone levels regulating satiation. Together, these techniques provide useful alternatives for patients in whom pharmacological or lifestyle modifications have proven ineffective. Despite these advantages, the long-term effects of these procedures on metabolic changes remain to be studied. Furthermore, the management of complications from these procedures continues to evolve. In this review, we aim to elaborate on the clinical indications and efficacy of the endobariatric procedures, together with various types of available endoscopic bariatric therapy procedures.

Lower gastrointestinal endoscopy has evolved over time, fulfilling a widening diagnostic and therapeutic remit. As our understanding of colorectal cancer and its prevention has improved, endoscopy has progressed with improved diagnostic technologies and advancing endoscopic therapies. Despite this, the fundamental design of the endoscope has remained similar since its inception. This review presents the important role lower gastrointestinal endoscopy serves in the prevention of colorectal cancer and the desirable characteristics of the endoscope that would enhance this. A brief history of the endoscope is presented. Current and future robotic endoscopic platforms, which may fulfil these desirable characteristics, are discussed. The incorporation of new technologies from allied scientific disciplines will help the endoscope fulfil its maximum potential in preventing the increasing global burden of colorectal cancer. There are a number of endoscopic platforms under development, which show significant promise.

Objective: The endoscopic findings associated with Helicobacter pylori-naïve status, current infection or past infection are an area of ongoing interest. Previous studies have investigated parameters with a potential diagnostic value. The aim of this study was to perform meta-analysis of the available literature to validate the diagnostic accuracy of mucosal features proposed in the Kyoto classification.

Data sources: The databases of MEDLINE and Embase, clinicalTrials.gov and the Cochrane Library were systematically searched for relevant studies from October 1999 to October 2019.

Methods: A bivariate random effects model was used to produce pooled diagnostic accuracy calculations for each of the studied endoscopic findings. Diagnostic odds ratios and sensitivity and specificity characteristics were calculated to identify significant predictors of H pylori status.

Results: Meta-analysis included 4380 patients in 15 studies. The most significant predictor of an H pylori-naïve status was a regular arrangement of collecting venules (diagnostic odds ratio 55.0, sensitivity 78.3%, specificity 93.8%). Predictors of active H pylori infection were mucosal oedema (18.1, 63.7%, 91.1%) and diffuse redness (14.4, 66.5%, 89.0%). Map-like redness had high specificity for previous H pylori eradication (99.0%), but poor specificity (13.0%).

Conclusion: The regular arrangement of collecting venules, mucosal oedema, diffuse redness and map-like redness are important endoscopic findings for determining H pylori status. This meta-analysis provides a tentative basis for developing future endoscopic classification systems.

Endoscopic submucosal dissection (ESD) is a minimally invasive therapeutic procedure to remove larger polyps or early non-metastatic lesions. It has long been used in Asia, but is now fast growing in popularity in the West. There are multiple challenges faced by ESD practitioners. While the practice of ESD in gastric lesions is relatively well established, the oesophagus with its narrow lumen and challenging workspace, and the colon with its tortuous course and folds are more challenging frontiers. The nature of performing a procedure endoscopically means that conventional methods offer no mechanism for providing counter-traction while performing dissection, impeding visibility and increasing the rate of complications. There are a multitude of tools available to those performing ESD for the different stages of the procedure. This article reviews the accessories currently used in regular ESD practice including the knives used to cut and dissect lesions, the cap and hood devices used to improve visibility and safety, injection fluids to lift the submucosal plane, haemostatic devices, generators, and finally, emerging traction apparatus. There is some evidence behind the use of these tools, however, ESD remains the domain of a small number of practitioners and the practice relies heavily on expert experience. Evolution of the ESD toolbox will make the procedure more accessible to more endoscopists, which in turn will drive the development of a more substantial evidence base to evaluate efficacy and safety of the multitude of tools.

The rise of endoscopic techniques allowing weight loss offers an attractive alternative to surgical interventions in Western countries where the obesity epidemic has risen dramatically. Endoscopists are well positioned to manage obesity given their broad-based medical knowledge, understanding of gastrointestinal physiology, and training in endoscopic technique. The field of bariatric and metabolic endoscopy has permitted the development of several efficacious and safe technologies. This review focuses on techniques and devices used for endoscopic management of obesity, as well as the fundamental justifications to offer those therapies to obese patients.

Endoscopic therapy of early Barrett's oesophagus-related neoplasia is the treatment of choice for low-grade-dysplasia, high-grade dysplasia and mucosal Barrett's cancer. Low-grade-dysplasia without any visible lesion should be ablated, preferably with radiofrequency ablation. In cases with the presence of a visible lesion, high-grade dysplasia and early Barrett's adenocarcinoma, endoscopic resection techniques like multiband ligation endoscopic resection or endoscopic submucosal dissection should be applied. After complete resection of all visible neoplastic lesions, ablation of the remaining Barrett's oesophagus should be performed to prevent recurrence. Ablation techniques available are radiofrequency ablation, argon plasma coagulation and cryoablation.

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.