{"id":337713,"date":"2025-12-10T12:57:32","date_gmt":"2025-12-10T12:57:32","guid":{"rendered":"https:\/\/peraltafinancing.com\/uncategorized\/dispatch-from-the-digital-health-frontier-gastroenterology-embraces-artificial-intelligence\/"},"modified":"2025-12-10T12:57:32","modified_gmt":"2025-12-10T12:57:32","slug":"dispatch-from-the-digital-health-frontier-gastroenterology-embraces-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=337713","title":{"rendered":"Dispatch from the Digital Health Frontier: Gastroenterology Embraces Artificial Intelligence"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<h4 style=\"text-align: left;\">AI and machine learning have the potential to redefine<br \/>\nthe management of several GI disorders.<\/h4>\n<div class=\"separator\" style=\"clear: both; text-align: center;\"><a href=\"https:\/\/blogger.googleusercontent.com\/img\/b\/R29vZ2xl\/AVvXsEgpylgIwR5Iz_zwg9SHNkPAou2_7QPmdwnIlJZyu2oek1zUYik-8tGp2Ttxgm-2LWqFmVmoh7bdnZwsOz-bZJwV290CVmioVchbhAkWLP8vMIscDDXo_u6SLvsJ7yxzIQUMgyh3b9cAcOcU\/s800\/shutterstock_1452312035.jpg\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\"><img loading=\"lazy\" decoding=\"async\" border=\"0\" data-original-height=\"514\" data-original-width=\"800\" height=\"258\" src=\"https:\/\/blogger.googleusercontent.com\/img\/b\/R29vZ2xl\/AVvXsEgpylgIwR5Iz_zwg9SHNkPAou2_7QPmdwnIlJZyu2oek1zUYik-8tGp2Ttxgm-2LWqFmVmoh7bdnZwsOz-bZJwV290CVmioVchbhAkWLP8vMIscDDXo_u6SLvsJ7yxzIQUMgyh3b9cAcOcU\/w400-h258\/shutterstock_1452312035.jpg\" width=\"400\"\/><\/a><\/div>\n<p><i>John Halamka, M.D., president, Mayo Clinic Platform, and<br \/>\nPaul Cerrato, senior research analyst and communications specialist, Mayo<br \/>\nClinic Platform, wrote this article.<\/i><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">Colonoscopy is one of the true success stories in modern<br \/>\nmedicine. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/23784448\/\">Studies<\/a> have demonstrated that colonoscopy<br \/>\nscreening detects the cancer at a much earlier stage, reducing the risk of<br \/>\ninvasive tumors and metastatic disease, and <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/29532085\/\">reducing mortality<\/a>. However, while colorectal cancer is highly<br \/>\npreventable, it is the <a href=\"https:\/\/www.cdc.gov\/cancer\/colorectal\/statistics\/index.htm\">third leading<br \/>\ncause of cancer-related deaths<\/a><br \/>\nin the U.S. About 148,000 individuals develop the malignancy and over 53,000<br \/>\ndie from it each year. We asked ourselves a question: can AI improve the<br \/>\ndetection of this and related gastrointestinal disorders?<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">As we explained in <a href=\"https:\/\/www.routledge.com\/The-Digital-Reconstruction-of-Healthcare-Transitioning-from-Brick-and-Mortar\/Cerrato-Halamka\/p\/book\/9780367555979\"><i>The<br \/>\nDigital Reconstruction of Healthcare<\/i><\/a><i>, <\/i>one of the challenges in making an accurate diagnosis of GI disease is<br \/>\ndifferentiating between disorders that look similar at the cellular level. For<br \/>\nexample, because environmental enteropathy and celiac disease overlap histopathologically,<br \/>\ndeep learning algorithms have been designed to analyze biopsy slides to detect<br \/>\nthe subtle differences between the two conditions. Syed et al.<sup>1<\/sup> used<br \/>\na combination of convolutional and deconvolutional neural networks in a<br \/>\nprospective analysis of over 3,000 biopsy images from 102 children. They were<br \/>\nable to tell the differences between environmental enteropathy, celiac disease,<br \/>\nand normal controls with an accuracy rating of 93.4%, and a false negative rate<br \/>\nof 2.4%. Most of these mistakes occurred when comparing celiac patients to<br \/>\nhealthy controls.<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">The investigators also identified several biomarkers that<br \/>\nmay help separate the two GI disorders: interleukin 9, interleukin 6,<br \/>\ninterleukin 1b, and interferon-induced protein 10 were all helpful in making an<br \/>\naccurate prediction regarding the correct diagnosis. The potential benefits to<br \/>\nthis deep learning approach become obvious when one considers the arduous<br \/>\nprocess that patients have to endure to reach a definitive diagnosis of either<br \/>\ndisorder: typically, they must undergo 4 to 6 biopsies and may need several<br \/>\nendoscopic procedures to sample various sections of the intestinal tract<br \/>\nbecause the disorder may affect only specific areas along the lining and leave<br \/>\nother areas intact.<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">Several randomized controlled trials have been conducted<br \/>\nto support the use of ML in gastroenterology. Chinese investigators, working in<br \/>\nconjunction with Beth Israel Deaconess Medical Center and Harvard Medical<br \/>\nSchool, tested a convolutional neural network to determine if it was capable of<br \/>\nimproving the detection of precancerous colorectal polyps in real time.<sup>2<\/sup><br \/>\nThe need for a better system of detecting these growths is evident, given the<br \/>\nfact that more than 1 in 4 adenomas are missed during coloscopies. To address<br \/>\nthe problem, Wang et al. randomized more than 500 patients to routine<br \/>\ncolonoscopy and more than 500 to computer-assisted colonoscopies. In the final<br \/>\nanalysis, the adenoma detection rate (ADR) was higher in the ML-assisted group<br \/>\n(29.1% vs. 20.3%, P &lt; 0.001). The higher ADR occurred because the algorithm<br \/>\nwas capable of detecting a greater number of smaller adenomas (185 vs. 102).<br \/>\nThere were no significant differences in the detection of large polyps.<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">Nayantara Coelho-Prabhu, M.D., a gastroenterologist at Mayo<br \/>\nClinic, points out, however, that the clinical relevance of detection of<br \/>\ndiminutive polyps remains to be determined. \u201cYet, there is definite clinical<br \/>\nimportance in the subsequent development of computer assisted diagnosis (CADx) or<br \/>\npolyp characterization algorithms. These will help clinicians determine<br \/>\nclinically relevant polyps, and possibly advance the resect and discard<br \/>\npractice. It also will help clinicians adequately assess margins of polyps, so<br \/>\nthat complete removal can be achieved, thus decreasing future recurrences.\u201d<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">Randomized clinical trials demonstrated that a convolutional<br \/>\nneural network in combination with deep reinforcement learning (collectively<br \/>\ncalled the WISENSE system) can reduce the number of blind spots during<br \/>\nendoscopy intended to evaluate the esophagus, stomach, and duodenum in real<br \/>\ntime. \u201cA total of 324 patients were recruited and randomized; 153 and 150<br \/>\npatients were analysed in the WISENSE and control group, respectively. Blind<br \/>\nspot rate was lower in WISENSE group compared with the control (5.86% vs<br \/>\n22.46%, p&lt;0.001) . . .\u201d<sup>3<\/sup><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">Mayo Clinic\u2019s Endoscopy Center, utilizing Mayo Clinic<br \/>\nPlatform\u2019s resources, has also been exploring the value of machine learning in<br \/>\nGI care with the assistance of Endonet, a comprehensive library of endoscopic<br \/>\nvideos and images, linked to clinical data including symptoms, diagnoses,<br \/>\npathology, and radiology. These data will include unedited full-length videos as<br \/>\nwell as video summaries of the procedure including landmarks, specific abnormalities,<br \/>\nand anatomical identifiers. Dr. Coelho-Prabhu explains that the idea is to have<br \/>\ndifferent user interfaces:\u00a0<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">\u201cFrom the patient\u2019s perspective, it will serve as an<br \/>\nelectronic video record of all their procedures, and future procedures can be<br \/>\ntailored to survey prior abnormal areas as needed.<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\"><span style=\"color: black; mso-themecolor: text1;\">From a research perspective, this will be a diverse and<br \/>\nrich library including large volumes of specialized populations such as<br \/>\nBarrett\u2019s esophagus, inflammatory bowel disease, familial polyposis syndromes.<br \/>\nThe additional strength is that Mayo Clinic provides highly specialized care,<br \/>\nespecially to these select populations. We can develop AI algorithms to advance<br \/>\nmedical care using this library. From a hospital system perspective, this would<br \/>\nserve as a reference library, guiding endoscopists, including for advanced<br \/>\ntherapeutic procedures in the future. It also could be used to measure and<br \/>\nmonitor quality indicators in endoscopy. From an educational standpoint, this<br \/>\nlibrary can be developed into a teaching set for both trainee and advanced<br \/>\npractitioners looking for CME opportunities. From industry perspective, this<br \/>\ndatabase could be used to train\/validate commercial AI algorithms.\u201d<o:p\/><\/span><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\">AI and machine learning may not be the panacea some<br \/>\ntechnology enthusiasts imagine it to be, but there\u2019s little doubt they are<br \/>\nbecoming an important partner in the road to more personalized patient care.<\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\"><b>References<\/b><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\"><span style=\"line-height: 150%; text-indent: -0.5in;\"><span style=\"font-family: inherit;\">1. Syed<br \/>\nS, Al-Bone M, Khan MN, et al. Assessment of machine learning detection of<br \/>\nenvironmental enteropathy and celiac disease in children. <i>JAMA Network Open.<br \/>\n<\/i>2019;2:e195822.<\/span><\/span><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\"><span style=\"text-indent: -0.5in;\"><span style=\"font-family: inherit;\">2. Wang<br \/>\nP, Berzin TM, Brown JR, et al. Real-time automatic detection system increases colonoscopic<br \/>\npolyp and adenoma detection rates: a prospective randomised controlled study.<br \/>\nGut. 2019;68:1813\u20131819.<\/span><\/span><\/p>\n<p class=\"MsoNormal\" style=\"line-height: 150%;\"><span style=\"color: black; mso-themecolor: text1;\"><span style=\"font-family: inherit;\">3. Wu L,<br \/>\nZhang J, Zhou W, et al Randomised controlled trial of WISENSE, a real-time<br \/>\nquality improving system for monitoring blind spots during esophagogastroduodenoscopy.<br \/>\nGut. 2019;68:2161\u20132169.<\/span><\/span><\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>AI and machine learning have the potential to redefine the management of several GI disorders. John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Colonoscopy is one of the true success stories in modern medicine. Studies have demonstrated that colonoscopy screening detects [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":337714,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[2973,824,22285,22564,2105,112467,597,13033],"dealstore":[],"offerexpiration":[],"class_list":["post-337713","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-artificial","tag-digital","tag-dispatch","tag-embraces","tag-frontier","tag-gastroenterology","tag-health","tag-intelligence"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Dispatch from the Digital Health Frontier: Gastroenterology Embraces Artificial Intelligence - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=337713\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Dispatch from the Digital Health Frontier: Gastroenterology Embraces Artificial Intelligence - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"AI and machine learning have the potential to redefine the management of several GI disorders. John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Colonoscopy is one of the true success stories in modern medicine. 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John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Colonoscopy is one of the true success stories in modern medicine. 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