New endoscopic tools in inflammatory bowel disease

Endoscopic remission is now considered the ultimate long‐term goal for treating inflammatory bowel disease (IBD). Recent advances in endoscopic techniques have progressively added new tools to the armamentarium of endoscopists for a deeper assessment and characterisation of the intestinal mucosa. Virtual Electronic chromoendoscopy is widely available in the endoscopic units, leading to a more accurate evaluation of the vascular and mucosal architecture of the colon, reducing the gap with histology, which is considered a favourable long‐term measure. In addition, advanced, sophisticated techniques such as endocytoscope and confocal laser endomicroscopy provide insights into individualised and personalised IBD therapy. Finally, high expectations are placed on the advent of Artificial Intelligence (AI) with promising applications that have the potential to revolutionise IBD diagnosis and management. Here, we discuss state‐of‐the‐art of endoscopic techniques and their applicability to accurate assess endoscopic and histological remission, predict response to therapy and detect, characterise and guide treatment of colonic dysplastic lesions. We are seeing the dawn of a new era wherein the applications of these new endoscopic tools, hand in hand with AI, offer the most incredible opportunity to deliver precision medicine to patients with IBD

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

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

VR-Caps: A Virtual Environment for Capsule Endoscopy

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-world data is challenging to obtain. Physically-realistic simulations providing synthetic data have emerged as a solution to the development of data-driven algorithms. In this work, we present a comprehensive simulation platform for capsule endoscopy operations and introduce VR-Caps, a virtual active capsule environment that simulates a range of normal and abnormal tissue conditions (e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope designs (e.g., mono, stereo, dual and 360{\deg}camera), and the type, number, strength, and placement of internal and external magnetic sources that enable active locomotion. VR-Caps makes it possible to both independently or jointly develop, optimize, and test medical imaging and analysis software for the current and next-generation endoscopic capsule systems. To validate this approach, we train state-of-the-art deep neural networks to accomplish various medical image analysis tasks using simulated data from VR-Caps and evaluate the performance of these models on real medical data. Results demonstrate the usefulness and effectiveness of the proposed virtual platform in developing algorithms that quantify fractional coverage, camera trajectory, 3D map reconstruction, and disease classification.Comment: 18 pages, 14 figure

Use of Secure Messaging By United States Veterans and Significant Others

ABSTRACT USE OF SECURE MESSAGING BY UNITED STATES VETERANS AND SIGNIFICANT OTHERS By Claudia S. Derman The University of Wisconsin-Milwaukee, 2014 Under the Supervision of Professor Karen H. Morin, PhD, RN, ANEF, FAAN The purpose of this study was to describe the topics discussed using secure messaging (SM), the pattern of use of SM, and whether the themes discussed and/or the pattern of use varied based on gender and age of the SM user. Secure messaging is an example of a technology that focuses on patient-centered communication. Secure messaging allows patients to communicate with their clinicians using the Internet and at their convenience, while maintaining the privacy of the information exchanged. Secure messages, if approved by the patient, may also be written by family members or significant others for the patient. By its nature, the use of SM is indicative of an individual\u27s involvement in their healthcare, utilizing self-management skills. Few studies were found that reported on the content of messages written by patients or their families. No studies were found that reviewed the topics patients write about in these secure messages nor were studies found that tracked the number of messages written by patients and relating to the days and time that were most utilized. A review of 1200 secure messages written by veterans and their caregivers was undertaken to determine what information was contained within the secure messages. The 1200 messages contained 1720 themes that were grouped using content analysis to yield a total of ten topics. The day of week and the time of day of messages were additionally reviewed by gender and age of the individual. Messages written by friends of family members were reviewed and compared to those written by patients. The topic most addressed as that of medications, with more than one-third of the 1720 themes within messages relating to medications. Veterans aged 55 to 64 years were the greatest users of the SM system followed closely by those between the ages of 65 to 74. Men wrote most frequently about medications while women wrote more themes related to the topics of complaints and concerns and consultations with specialists. Pattern of use of relative to time of day and day of the week was also reviewed in subset of the sample (n= 600). The most common time frame during which messages were sent was between 9:00 a.m. and 6 p.m., accounting for more than 70% of all messages. Tuesdays and Thursdays were the most often utilized days of week for SM. The implications of this study include revisiting how MyHealtheVet is configured to enhance the veteran\u27s ability to communicate effectively and appropriately with healthcare providers. It is possible that participants employed SM rather than other identified means to contact providers as they were assured of a response within a defined period of time. Findings have implications for users, clinicians, hospital administrators, and technical staff. The purposes of SM can be revisited with users, clinicians may wish to consider alternative strategies, and administrators may wish to revisit the current structure in terms of identifying a method to sort the information contained in SM