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Gastroenterology


Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning – Digital Health and Patient Safety Platform

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Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability. Several studies developed deep-learning (DL) algorithms classifying invasion depth of gastric cancers. Nevertheless, these algorithms are intended to be used after definite diagnosis of gastric cancers, which is not always feasible in various gastric neoplasms.


'World's first' magnetic robotic-assisted surgeries performed with Levita Magnetics' newest platform

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Levita Magnetics says "the first ever" robotic-assisted surgical procedures have been performed using the company's newest system in development, the Levita Robotic Platform. The first case was a reduced-incision laparoscopic cholecystectomy (gallbladder removal) completed by Dr Ignacio Robles, a minimally invasive surgeon at Clínica INDISA in Santiago, as part of a current clinical study of the system in Chile. The new robotic platform is intended to deliver the clinical benefits of the company's first commercial product, the Levita Magnetic Surgical System, including less pain, faster recovery and fewer scars for patients. The platform is intended to improve visualization, maintain surgeon control of instruments, and increase hospital efficiency with fewer assistive personnel required to conduct the procedures. With its compact footprint, the robotic platform is specially designed for high volume ambulatory or same-day discharge abdominal surgeries.


A Robot That Finds Your Lost Stuff and More AI-Enabled Gadgets to Come

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Researchers at Stanford University have developed a prototype toilet that uses an artificial intelligence-trained camera to track the form of feces and monitor the color and flow of urine. A "lab-on-a-chip" device built into the toilet will analyze micro stool samples to detect viruses like Covid-19 and blood, says Seung-min Park, the lead researcher on the project. This digital diary could yield valuable health insights and facilitate early, noninvasive diagnosis of irritable bowel syndrome or colorectal cancer, Dr. Park says. A look at how innovation and technology are transforming the way we live, work and play. An app would allow users to track health parameters.


Artificial intelligence could be new blueprint for precision drug discovery

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The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation's health care systems. "Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of'big data' and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s," said Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine. "This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don't translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program."


Image-Based Deep Learning Models Can Predict Abdominal Surgery Outcomes: Study

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Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text or sound. Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. With this background, researchers carried out a study to examine whether deep learning models (DLMs) using routine preoperative imaging can predict surgical complexity and outcomes in abdominal wall reconstruction. They applied image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020.



Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease

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Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study a...


Role of Artificial Intelligence in Video Capsule Endoscopy

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Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.


'Smart Toilet' Uses Artificial Intelligence to Monitor Bowel Health

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An artificial intelligence tool being developed by Duke scientists can be added to the standard toilet to help analyze patients' stool and give gastroenterologists the information they need to provide appropriate treatment for chronic issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). The work is being done by Duke University's Center for Water, Sanitation, Hygiene and Infectious Disease (WaSH-AID), and was presented Saturday at the virtual conference Digestive Disease Week 2021. "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, associate professor of medicine at Duke University and one of the lead authors on the study. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process," Fisher said. "The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."


Docbot lands a healthy $4M in series A financing

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A check up by Khosla Ventures determined that Docbot Inc. was healthy enough for the prominent biotech investor to take the lead in a $4 million series A round. The new funds bring the artificial intelligence company to a total of $8.5 million in capital raised to date. Other participants included Bold Capital Partners, Collaborative Fund and Boutique Venture Partners. Docbot's platform, Ultivision AI, uses artificial intelligence to enhance detection of gastrointestinal (GI) disease. The Irvine, Calif.-based company is targeting identification and classification of polyps, Barrett's esophagus, and ulcerative colitis to start.