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Gastroenterology


Health Care AI Systems Are Biased

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Thanks to advances in artificial intelligence (AI) and machine learning, computer systems can now diagnose skin cancer like a dermatologist would, pick out a stroke on a CT scan like a radiologist, and even detect potential cancers on a colonoscopy like a gastroenterologist. These new expert digital diagnosticians promise to put our caregivers on technology's curve of bigger, better, faster, cheaper. But what if they make medicine more biased too? At a time when the country is grappling with systemic bias in core societal institutions, we need technology to reduce health disparities, not exacerbate them. We've long known that AI algorithms that were trained with data that do not represent the whole population often perform worse for underrepresented groups.


Ranga Chandra Gudivada PhD on LinkedIn: Docbot Announces Results of Study Evaluating Deep Learning Platform

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We are excited to announce that results from our study in collaboration with Eli Lilly and Company was published today online in the leading journal in the field, #Gastroenterology. The study is the first to demonstrate that a deep learning #AI can be trained for automated disease severity scoring in patients with ulcerative colitis. This represents an opportunity to introduce machine reading of endoscopic videos into #IBD/ulcerative colitis clinical trials. Thank you to all our study authors!


Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation

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Early diagnosis of colorectal cancer substantially improves survival. However, over half of cases are diagnosed late due to the demand for colonoscopy—the ‘gold standard’ for screening—exceeding capacity. Colonoscopy is limited by the outdated design of conventional endoscopes, which are associated with high complexity of use, cost and pain. Magnetic endoscopes are a promising alternative and overcome the drawbacks of pain and cost, but they struggle to reach the translational stage as magnetic manipulation is complex and unintuitive. In this work, we use machine vision to develop intelligent and autonomous control of a magnetic endoscope, enabling non-expert users to effectively perform magnetic colonoscopy in vivo. We combine the use of robotics, computer vision and advanced control to offer an intuitive and effective endoscopic system. Moreover, we define the characteristics required to achieve autonomy in robotic endoscopy. The paradigm described here can be adopted in a variety of applications where navigation in unstructured environments is required, such as catheters, pancreatic endoscopy, bronchoscopy and gastroscopy. This work brings alternative endoscopic technologies closer to the translational stage, increasing the availability of early-stage cancer treatments. Magnetic endoscopes have the potential to improve access, reduce patient discomfort and enhance safety. While navigation of magnetic endoscopes can be challenging for the operator, a new approach by Martin, Scaglioni and colleagues explores how to reduce this burden by offering different levels of autonomy in robotic colonoscopy.


Robot that can perform colonoscopies aims to make it less unpleasant

New Scientist - News

A robot that can perform colonoscopies may make the procedure simpler and less unpleasant. Pietro Valdastri at the University of Leeds in the UK and his colleagues have developed a robotic arm that uses a machine learning algorithm to move a flexible probe along the colon. The probe is a magnetic endoscope, a tube with a camera lens at the tip, that the robot controls via a magnet external to the body. The system can either work autonomously or be controlled by a human operator using a joystick, which pushes the endoscope tip further along the colon. The system also keeps track of the location and orientation of the endoscope inside the colon.


Robot that can perform colonoscopies aims to make it less unpleasant

New Scientist

A robot that can perform colonoscopies may make the procedure simpler and less unpleasant. Pietro Valdastri at the University of Leeds in the UK and his colleagues have developed a robotic arm that uses a machine learning algorithm to move a flexible probe along the colon. The probe is a magnetic endoscope, a tube with a camera lens at the tip, that the robot controls via a magnet external to the body. The system can either work autonomously or be controlled by a human operator using a joystick, which pushes the endoscope tip further along the colon. The system also keeps track of the location and orientation of the endoscope inside the colon.


Robots: Scientists invent a mechanical arm to perform colonoscopies in 'less painful' procedure

Daily Mail - Science & tech

An AI-powered robotic arm can perform'less painful' colonoscopies to check for bowel cancer by using a magnet to externally steer a camera probe through the gut. The system -- from a team led from Leeds -- could prove to be the first major update in decades to the procedure, which is used some 100,000 times each year in the UK. In a colonoscopy, a long, thin, camera-ended probe is passed through the rectum and colon to hunt for and remove abnormalities and take tissue samples. The examination can be uncomfortable for the patient -- and requires highly skilled doctors to be performed, limiting the availability of the procedure. The artificially intelligent system, however, will aid less experienced doctors and nurses in safely guiding the probe to precise locations within the colon.


Gastroenterology & Artificial Intelligence Global Summit

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ASGE Assurance to High Level Scientific Evidence and Adherence to Neutrality: ASGE is committed to you, our learners. We are committed to assuring the highest level of scientific rigor, research-based evidence and practicality to clinical use is conveyed throughout this educational activity. Members who designed this educational activity are carefully selected for their content expertise, teaching excellence and neutrality on the topic(s) being presented. ASGE requires all subject matter experts, committee members and staff planners who are in a position to control any aspect of content to disclose all relevant financial relationships with any commercial interests. Financial relationships in any amount is considered a potential conflict of interest, is peer-reviewed and resolved according to ASGE's Educational Conflict of Interest policy and procedure prior to the start of this program.


Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

arXiv.org Artificial Intelligence

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users, based on the Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease self-diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph, resulting in better reasoning ability of our HealGCN model, when confronting users with scarce descriptions. At last, we validate our model on a large-scale EHR dataset, the superior performance does confirm our model's effectiveness in practice.


Researchers use machine learning to tackle food poisoning

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Scientists at a university in Scotland have developed a technique which could help to identify the source of food poisoning in a better way than curre


Amazon.com: Scope Forward: The Future of Gastroenterology Is Now in Your Hands eBook: Suthrum, Praveen: Kindle Store

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"Scope Forward is a complete study allowing providers to grasp and conquer the previously shadowy realm of the business of medicine. This text is a must for healthcare leaders charting their course to success." Reed B. Hogan, GI Associates and Endoscopy Center, Mississippi "What Praveen has done in his book Scope Forward is to illuminate us about a whole range of issues pertinent to the future of GI, from artificial intelligence to private equity. This is a very welcome entry into the'must read' category for all those involved in Gastroenterology. You will learn a lot!" --Dr.