New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques

Gastrointestinal (GI) bleeding is very common in humans, which may lead to fatal consequences. GI bleeding can usually be identified using a flexible wired endoscope. In 2001, a newer diagnostic tool, wireless capsule endoscopy (WCE) was introduced. It is a swallow-able capsule-shaped device with a camera that captures thousands of color images and wirelessly sends those back to a data recorder. After that, the physicians analyze those images in order to identify any GI abnormalities. But it takes a longer screening time which may increase the danger of the patients in emergency cases. It is therefore necessary to use a real-time detection tool to identify bleeding in the GI tract. Each material has its own spectral ‘signature’ which shows distinct characteristics in specific wavelength of light [33]. Therefore, by evaluating the optical characteristics, the presence of blood can be detected. In the study, three main hardware designs were presented: one using a two-wavelength based optical sensor and others using two six-wavelength based spectral sensors with AS7262 and AS7263 chips respectively to determine the optical characteristics of the blood and non-blood samples. The goal of the research is to develop a machine learning model to differentiate blood samples (BS) and non-blood samples (NBS) by exploring their optical properties. In this experiment, 10 levels of crystallized bovine hemoglobin solutions were used as BS and 5 food colors (red, yellow, orange, tan and pink) with different concentrations totaling 25 non-blood samples were used as NBS. These blood and non-blood samples were also combined with pig’s intestine to mimic in-vivo experimental environment. The collected samples were completely separated into training and testing data. Different spectral features are analyzed to obtain the optical information about the samples. Based on the performance on the selected most significant features of the spectral wavelengths, k-nearest neighbors algorithm (k-NN) is finally chosen for the automated bleeding detection. The proposed k-NN classifier model has been able to distinguish the BS and NBS with an accuracy of 91.54% using two wavelengths features and around 89% using three combined wavelengths features in the visible and near-infrared spectral regions. The research also indicates that it is possible to deploy tiny optical detectors to detect GI bleeding in a WCE system which could eliminate the need of time-consuming image post-processing steps

Detecting Crohn’s disease from high resolution endoscopy videos: the thick data approach

Detecting diseases in high resolution endoscopy videos can be done in several ways depending on the methodology for detection. One such method that has been a hot topic in the field of medical technology research is the implementation of machine learning techniques to aid in the diagnosis of networks. While, this has been studied extensively with traditional machine learning methods and more recently neural networks, major issues persist in their implementation in everyday health. Among the largest issues is the size of the training data needed to make accurate prediction, as well as the inability to generalize the networks to several disease. We address these issues with a novel approach to detecting Inflammatory bowel diseases, specifically Crohn’s disease in endoscopy videos. We use thick data analytics to teach a network to detect heuristics of a disease, not to simply make classifications from images. Using heuristic annotations like bounding boxes and segmentation masks, we train a Siamese neural network to detect video frames for ulcers, polyps, erosions, and erythema with accuracies as high as 87.5% for polyps and 77.5% for ulcers. We then implement this network in a protype frontend that physicians can use to upload videos and receive the processed images in an interactive format. We also pontificate as to how our approach and prototype can be expanded to several diseases with learning of more heuristics

Magnetically Assisted Capsule Endoscopy: A Viable Alternative to Conventional Flexible Endoscopy of the Stomach?

INTRODUCTION: Oesophagogastroduodenoscopy is the investigation of choice to identify mucosal lesions of the upper gastrointestinal tract, but it is poorly tolerated by patients. A simple non-invasive technique to image the upper gastrointestinal tract, which could be made widely available, would be beneficial to patients. Capsule endoscopy is well tolerated by patients but the stomach has proved difficult to visualise accurately with capsule technology due to its’ capacious nature and mucosal folds, which can obscure pathology. MiroCam Navi (Intromedic Ltd, Seoul, Korea) is a capsule endoscope containing a small amount of magnetic material which has been made available with a handheld magnet which might allow a degree of control. This body of work aims to address whether this new technology could be a feasible alternative to conventional flexible endoscopy of the stomach. METHODS: Four studies were conducted to test this research question. The first explores the feasibility of magnetically assisted capsule endoscopy of the stomach and operator learning curve in an ex vivo porcine model. This was followed by a randomised, blinded trial comparing magnetically assisted capsule endoscopy to conventional flexible endoscopy in ex vivo porcine stomach models. Subsequently a prospective, single centre randomised controlled trial in humans examined whether magnetically assisted capsule endoscopy could enhance conventional small bowel capsule endoscopy by reducing gastric transit time. Finally a blinded comparison of diagnostic yield of magnetically assisted capsule endoscopy compared to oesophagogastroduodenoscopy was performed in patients with recurrent or refractory iron deficiency anaemia. RESULTS: In the first study all stomach tags were identified in 87.2% of examinations and a learning curve was demonstrated (mean examination times for the first 23 and second 23 procedures 10.28 and 6.26 minutes respectively (p<0.001). In the second study the difference in sensitivities between oesophagogastroduodenoscopy and conventional flexible endoscopy for detecting beads within an ex vivo porcine stomach model was 1.11 (95% CI 0.06, 28.26) proving magnetically assisted capsule endoscopy to be non-inferior to flexible endoscopy. In the first human study, although there was no significant difference in gastric transit time or capsule endoscopy completion rate between the two groups (p=0.12 and p=0.39 respectively), the time to first pyloric image was significantly shorter in the intervention group (p=0.03) suggesting that magnetic control hastens capsular transit to the gastric antrum but cannot impact upon duodenal passage. In the last study, a total of 38 pathological findings were identified in this comparative study of magnetically assisted capsule endoscopy and conventional endoscopy. Of these, 16 were detected at both procedures, while flexible endoscopy identified 14 additional lesions not seen at magnetically assisted capsule endoscopy and magnetically assisted capsule endoscopy detected 8 abnormalities not seen by oesophagogastroduodenoscopy. No adverse events occurred in either of the human trials. Finally magnetically steerable capsule endoscopy induced less procedural pain, discomfort and distress than oesophagogastroduodenoscopy (p=0.0009, p=0.001 and p=0.006 respectively). CONCLUSION: Magnetically assisted capsule endoscopy is safe, well tolerated and a viable alternative to conventional endoscopy. Further research to develop and improve this new procedure is recommended

Small bowel capsule endoscopy : where are we after almost 15 years of use?

The development of capsule endoscopy (CE) in 2001 has given gastroenterologists the opportunity to investigate the small bowel in a non-invasive way. CE is most commonly performed for obscure gastrointestinal bleeding, but other indications include diagnosis or follow-up of Crohn's disease, suspicion of a small bowel tumor, diagnosis and surveillance of hereditary polyposis syndromes, Nonsteroidal anti-inflammatory drug-induced small bowel lesions and celiac disease. Almost fifteen years have passed since the release of the small bowel capsule. The purpose of this review is to offer the reader a brief but complete overview on small bowel CE anno 2014, including the technical and procedural aspects, the possible complications and the most important indications. We will end with some future perspectives of CE